Colorectal Cancer

Introduction

Colorectal cancer is dangerous and a confirmed diagnosis is almost like a death sentence considering it is the third most common malignancy diagnosed in the human population. Colorectal cancer affects the rectum and colon parts of the large intestine and begins from the glandular epithelial cells when some of these cells develop a series of genetic mutations (Rawla et al., 2019). Following mutations, the cell begins to multiply abnormally and increase in number giving rise to a benign adenoma, which may gradually progress into carcinoma and spread to other body tissues. The aggressive nature of the cancer and it ability to spread to adjacent tissues drastically reduces survival rates. The cancer affects men more than women, and it is 3–4 times fold in developed compared to developing countries (Rawla et al., 2019). Globally, at least 861,000 deaths and 1.8 million new incidences were reported in 2018 (GLOBOCAN 2018). In the United States, colorectal cancer appears as number three in the scale of the most lethal cancers. Furthermore, from 1995-2016 the incidence of colorectal cancer in American men and women below the age of 50 gradually rose at a rate of 2% (Siegel et al., 2020). In America the 5-year survival rate for patients with an early diagnosis is 90% but the survival rate drastically reduces up to 14% for late diagnosis. (American Cancer Society, 2020). Globally, the survival rate has also increased owing to better early screening and better treatments (Rawla et al., 2019).

The Cancer Genome Atlas (TCGA) colonic adenocarcinoma, (TCGA-COAD) and TCGA rectal adenocarcinoma (TCGA-READ) provide molecular knowledge of colorectal cancer. Data collection began in 2006 until 2012 when enough samples were accrued for processing. The samples were collected from all over the world through tissue source sites which include hospitals and health research centres in America, Europe, Asia, South America and Africa. The project used 276 samples and investigated exome patterns, DNA duplicate number, promoter methylation, and the expression of mRNA and microRNA (Cancer Genome Atlas Network (2012). Two hundred two colon samples and 75 rectum samples were analyzed. The age average of the individuals whose samples were analyzed in TCGA-COAD was 66.9, while TCGA-READ was 64.5 (Wang et al., 2018). In terms of race, TCGA-COAD had 215 whites, 59 blacks, and 11 from other ethnicities. TCGA-READ had 82 whites, six blacks, and one under other categories. There were slightly more males than females in both COAD and READ. The project identified more than 94% mutations in one or several members of the WNT signalling pathway.

Krüppel-like factor 5 (KLF5): plays an important function in controlling the growth of typical intestinal epithelial cells as well as colorectal cancer cells. Increased expression of KLF5 is associated with poor prognosis hence reduced survival rate of the patients with colorectal cancer (Tagaki et al., 2020). The mutation in the zinc finger transcription factor makes it difficult for the FBW7α to degrade it (Bialkowska et al., 2014). It promotes the growth of cancer stem like cells.

Ets2 gene: This gene has been identified as significant in the Wnt signaling pathway associated with the development of colorectal cancer. The expression of Ets2 occurs within intestinal crypts. The lack of Ets2 leads to elevated growth at the base of colon crypts. Impaired regulation Ets2 promotes cell proliferation in cancers, invasion and metastasis by activating gene transcription (Fry & Inoue, 2018). The gene is associated with poor survival rate and prognosis in patients with colorectal cancer.

SMAD2: This gene plays an important role in the transforming growth factor, TGF-β signaling pathway, which provides proliferation inhibitory signals in the typical intestinal epithelial cells (Jung et al., 2017). The lack of SMAD2 expression is associated with the spread of colorectal cancers and a reduced survival rate with a poor prognosis (Xie et al., 2003).

References

American Cancer Society. (2020). American Cancer Society. Cancer Facts & Figures 2020. Atlanta, Ga: American Cancer Society; 2020.

Bialkowska, A. B., Liu, Y., Nandan, M. O., & Yang, V. W. (2014). A colon cancer-derived mutant of Krüppel-like factor 5 (KLF5) is resistant to degradation by glycogen synthase kinase 3β (GSK3β) and the E3 ubiquitin ligase F-box and WD repeat domain-containing 7α (FBW7α). The Journal of biological chemistry, 289(9), 5997–6005. https://doi.org/10.1074/jbc.M113.508549

Cancer Genome Atlas Network (2012). Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487(7407), 330–337. https://doi.org/10.1038/nature11252

Fry, E. A., & Inoue, K. (2018). Aberrant expression of ETS1 and ETS2 proteins in cancer. Cancer reports and reviews, 2(3), 10.15761/CRR.1000151. https://doi.org/10.15761/CRR.1000151

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. https://gco.iarc.fr/

Jung B., Jonas J. S., and Beauchamp D. (2017). Transforming Growth Factor β Superfamily Signalingin Development of Colorectal Cancer. Gastroenterology 2017;152:36–52 https://www.gastrojournal.org/article/S0016-5085(16)35239-8/pdf

Rawla, P., Sunkara, T., & Barsouk, A. (2019). Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Przeglad gastroenterologiczny, 14(2), 89–103. https://doi.org/10.5114/pg.2018.81072

Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF et al. (2020). Anderson JC, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2020. Cancer J Clin.

Takagi Y, Sakai N, Yoshitomi H, et al. (2020).  High expression of Krüppel-like factor 5 is associated with poor prognosis in patients with colorectal cancer Cancer Sci. 2020;10.1111/cas.14411. doi:10.1111/cas.14411

Wang, X., Steensma, J.T., Bailey, M. et al. (2018). Characteristics of The Cancer Genome Atlas cases relative to U.S. general population cancer cases. Br J Cancer 119, 885–892 https://doi.org/10.1038/s41416-018-0140-8

Xie W, Rimm DL, Lin Y, Shih WJ, Reiss M. (2003). Loss of Smad signaling in human colorectal cancer is associated with advanced disease and poor prognosis. Cancer J.;9(4):302‐312. doi:10.1097/00130404-200307000-00013

Colorectal Cancer

Introduction

Colorectal cancer is dangerous and a confirmed diagnosis is almost like a death sentence considering it is the third most common malignancy diagnosed in the human population. Colorectal cancer affects the rectum and colon parts of the large intestine and begins from the glandular epithelial cells when some of these cells develop a series of genetic mutations (Rawla et al., 2019). Following mutations, the cell begins to multiply abnormally and increase in number giving rise to a benign adenoma, which may gradually progress into carcinoma and spread to other body tissues. The aggressive nature of the cancer and it ability to spread to adjacent tissues drastically reduces survival rates. The cancer affects men more than women, and it is 3–4 times fold in developed compared to developing countries (Rawla et al., 2019). Globally, at least 861,000 deaths and 1.8 million new incidences were reported in 2018 (GLOBOCAN 2018). In the United States, colorectal cancer appears as number three in the scale of the most lethal cancers. Furthermore, from 1995-2016 the incidence of colorectal cancer in American men and women below the age of 50 gradually rose at a rate of 2% (Siegel et al., 2020). In America the 5-year survival rate for patients with an early diagnosis is 90% but the survival rate drastically reduces up to 14% for late diagnosis. (American Cancer Society, 2020). Globally, the survival rate has also increased owing to better early screening and better treatments (Rawla et al., 2019).

. The Cancer Genome Atlas (TCGA) colonic adenocarcinoma, (TCGA-COAD) and TCGA rectal adenocarcinoma (TCGA-READ) provide molecular knowledge of colorectal cancer. Data collection began in 2006 until 2012 when enough samples were accrued for processing. The samples were collected from all over the world through tissue source sites which include hospitals and health research centres in America, Europe, Asia, South America and Africa. The project used 276 samples and investigated exome patterns, DNA duplicate number, promoter methylation, and the expression of mRNA and microRNA (Cancer Genome Atlas Network (2012). Two hundred two colon samples and 75 rectum samples were analyzed. The age average of the individuals whose samples were analyzed in TCGA-COAD was 66.9, while TCGA-READ was 64.5 (Wang et al., 2018). In terms of race, TCGA-COAD had 215 whites, 59 blacks, and 11 from other ethnicities. TCGA-READ had 82 whites, six blacks, and one under other categories. There were slightly more males than females in both COAD and READ. The project identified more than 94% mutations in one or several members of the WNT signaling pathway.

Krüppel-like factor 5 (KLF5): plays an important function in controlling the growth of typical intestinal epithelial cells as well as colorectal cancer cells. Increased expression of KLF5 is associated with poor prognosis hence reduced survival rate (Tagaki et al., 2020).  The mutation in the zinc finger transcription factor makes it difficult for the FBW7α to degrade it (Bialkowska et al., 2014). It promotes the growth of cancer stem like cells.

Ets2 gene: This gene has been identified as significant in the Wnt signaling pathway associated with the development of colorectal cancer. The expression of Ets2 occurs within intestinal crypts. The lack of Ets2 leads to elevated growth at the base of colon crypts. Impaired regulation Ets2 promotes cell proliferation in cancers, invasion and metastasis by activating gene transcription (Fry & Inoue, 2018) thus leading to poor survival rate in patients with colorectal cancer.

SMAD2: This gene plays an important role in the transforming growth factor, TGF-β signaling pathway, which provides proliferation inhibitory signals in the typical intestinal epithelial cells (Jung et al., 2017). The lack of SMAD2 expression is associated with the spread of colorectal cancers and a reduced survival rate (Xie et al., 2003).

References

American Cancer Society. (2020). American Cancer Society. Cancer Facts & Figures 2020. Atlanta, Ga: American Cancer Society; 2020.

Bialkowska, A. B., Liu, Y., Nandan, M. O., & Yang, V. W. (2014). A colon cancer-derived mutant of Krüppel-like factor 5 (KLF5) is resistant to degradation by glycogen synthase kinase 3β (GSK3β) and the E3 ubiquitin ligase F-box and WD repeat domain-containing 7α (FBW7α). The Journal of biological chemistry, 289(9), 5997–6005. https://doi.org/10.1074/jbc.M113.508549

Cancer Genome Atlas Network (2012). Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487(7407), 330–337. https://doi.org/10.1038/nature11252

Fry, E. A., & Inoue, K. (2018). Aberrant expression of ETS1 and ETS2 proteins in cancer. Cancer reports and reviews, 2(3), 10.15761/CRR.1000151. https://doi.org/10.15761/CRR.1000151

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. https://gco.iarc.fr/

Jung B., Jonas J. S., and Beauchamp D. (2017). Transforming Growth Factor β Superfamily Signalingin Development of Colorectal Cancer. Gastroenterology 2017;152:36–52 https://www.gastrojournal.org/article/S0016-5085(16)35239-8/pdf

Rawla, P., Sunkara, T., & Barsouk, A. (2019). Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Przeglad gastroenterologiczny, 14(2), 89–103. https://doi.org/10.5114/pg.2018.81072

Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF et al. (2020). Anderson JC, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2020. Cancer J Clin.

Takagi Y, Sakai N, Yoshitomi H, et al. (2020).  High expression of Krüppel-like factor 5 is associated with poor prognosis in patients with colorectal cancer Cancer Sci. 2020;10.1111/cas.14411. doi:10.1111/cas.14411

Wang, X., Steensma, J.T., Bailey, M. et al. (2018). Characteristics of The Cancer Genome Atlas cases relative to U.S. general population cancer cases. Br J Cancer 119, 885–892 https://doi.org/10.1038/s41416-018-0140-8

Xie W, Rimm DL, Lin Y, Shih WJ, Reiss M. (2003). Loss of Smad signaling in human colorectal cancer is associated with advanced disease and poor prognosis. Cancer J.;9(4):302‐312. doi:10.1097/00130404-200307000-00013

Colorectal Cancer

Introduction

Colorectal cancer is the third most common malignancy diagnosed in the human population. At least 861,000 deaths and 1.8 million new incidences were reported in 2018 (GLOBOCAN 2018). Colorectal cancer affects men more than women, and it is 3–4 times fold in developed compared to developing countries (Rawla et al., 2019). In the United States, colorectal cancer appears as number three in the scale of the most lethal cancers. Furthermore, from 1995-2016 the incidence of colorectal cancer in American men and women below the age of 50 gradually rose at a rate of 2% (Siegel et al., 2020). Colorectal cancer affects the rectum and colon parts of the large intestine and begins from the glandular epithelial cells when some of these cells develop a series of genetic mutations (Rawla et al., 2019). Following mutation, the cell begins to multiply abnormally and increase in number giving rise to a benign adenoma, which may gradually progress into carcinoma and spread to other body tissues. Understanding the different types of mutations arising from the glandular epithelial cells of the colorectal area is the first step toward generating targeted molecular therapies. This will help alleviate the health burden caused by these malignancies.

The Cancer Genome Atlas (TCGA) offers a comprehensive understanding of the molecular basis of cancers. TCGA colonic adenocarcinoma, (TCGA-COAD) and TCGA rectal adenocarcinoma (TCGA-READ) provide molecular knowledge of colorectal cancer. The project used 276 samples and investigated exome patterns, DNA duplicate number, promoter methylation, and the expression of mRNA and microRNA (Cancer Genome Atlas Network (2012). Two hundred two colon samples and 75 rectum samples were analyzed. The age average of the individuals whose samples were         analyzed in TCGA-COAD was 66.9, while TCGA-READ was 64.5 (Wang et al., 2018). In terms of race, TCGA-COAD had 215 whites, 59 blacks, and 11 from other ethnicities. TCGA-READ had 82 whites, six blacks, and one under other categories. There were slightly more males than females in both COAD and READ. The project identified more than 94% mutations in one or several members of the WNT signaling pathway.

Krüppel-like factor 5 (KLF5): plays an important function in controlling the growth of typical intestinal epithelial cells as well as colorectal cancer cells. A gene sequencing analysis of colorectal cancer tissues has led to the discovery of a somatic mutation (P301S) in KLF5 (Bialkowska et al., 2014). The mutation in the zinc finger transcription factor makes it difficult for the FBW7α to degrade it (Bialkowska et al., 2014). As such, the growth of intestinal epithelial cells continues unregulated.

Ets2 gene: This gene has been identified as significant in the Wnt signaling pathway associated with the development of colorectal cancer. The expression of Ets2 occurs within intestinal crypts. The T-cell factor (TCF) binding sites in the Ets2 promoter reveal a direct involvement in Wnt pathway whereas indirectly association is revealed in its regulation by the Achaete Scute-Like 2 transcription factor (Ascl2), which is a direct Wnt target in intestinal stem cells (Munera at al., 2011). The lack of Ets2 leads to elevated growth at the base of colon crypts.

SMAD2: This gene plays an important role in the transforming growth factor, TGF-β signaling pathway, which provides proliferation inhibitory signals in the typical intestinal epithelial cells (Jung et al., 2017). Mutations in SMAD2 hinder the formation of SMAD2- SMAD4 complex, which is critical in the inhibitory process. Therefore, mutations in SMAD2 block the tumor suppressor ability leading to colorectal cancer.

.

References

Bialkowska, A. B., Liu, Y., Nandan, M. O., & Yang, V. W. (2014). A colon cancer-derived mutant of Krüppel-like factor 5 (KLF5) is resistant to degradation by glycogen synthase kinase 3β (GSK3β) and the E3 ubiquitin ligase F-box and WD repeat domain-containing 7α (FBW7α). The Journal of biological chemistry, 289(9), 5997–6005. https://doi.org/10.1074/jbc.M113.508549

Cancer Genome Atlas Network (2012). Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487(7407), 330–337. https://doi.org/10.1038/nature11252

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. https://gco.iarc.fr/

Jung B., Jonas J. S., and Beauchamp D. (2017). Transforming Growth Factor β Superfamily Signalingin Development of Colorectal Cancer. Gastroenterology 2017;152:36–52 https://www.gastrojournal.org/article/S0016-5085(16)35239-8/pdf

Múnera, J., Ceceña, G., Jedlicka, P., Wankell, M., & Oshima, R. G. (2011). Ets2 regulates colonic stem cells and sensitivity to tumorigenesis. Stem cells (Dayton, Ohio), 29(3), 430–439. https://doi.org/10.1002/stem.599

Rawla, P., Sunkara, T., & Barsouk, A. (2019). Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Przeglad gastroenterologiczny, 14(2), 89–103. https://doi.org/10.5114/pg.2018.81072

Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF et al. (2020). Anderson JC, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2020. Cancer J Clin.

Wang, X., Steensma, J.T., Bailey, M. et al. (2018). Characteristics of The Cancer Genome Atlas cases relative to U.S. general population cancer cases. Br J Cancer 119, 885–892 https://doi.org/10.1038/s41416-018-0140-8

Dual task

 

The results recorded in 3 trials

 

Number of Trials Stimulus Duration (ms) Box speed (Px/sec) Trial Duration (ms) Box Width (px) Average Hit Average Miss Average False Alarm Average Tracking Error
3 2000 250 25000 23 0.00 1 0.00 1

 

I have set up a google sheet for you to post your data.  Here is the link.  It looks like this:

 

 

 

 

Number of Trials Stimulus Duration (ms) Box speed (Px/sec) Trial Duration (ms) Box Width (px) Average Hit Average Miss Average False Alarm Average Tracking Error
3 2000 250 25000 23 0.00 1 0.00 1

 

Dual task

 

 

 

Dual Task Statistical Analysis

Student’s Name

Institutional Affiliation

 

 

Abstract

The ability to perform an activity depends on the mental cognitive aspect of a person. Both succeeding and failing with respect to a dual task perspective can be determined and explained using statistical analysis. Numerous psychological theories have explored the concepts associated with the ability of a person to perform two or more activities. Most of them have suggest that the capability is determined by the available sources of information to utilize fully (Vergauwe, Barrouillet & Camos, 2010). In addition, both verbal and visuospatial aspects are affected by the available resources in any given background. This study mainly focused on 4 major dual task simulations where all the participants ensured that they kept an equilibrium in both verbal and visuospatial aspects respectively. The cognitive capability was modelled such that for each dual task, the ability to recall things was noted and analyzed using ANOVA. The results indicated that verbal and visual spatial dimensions were inversely proportional with the cognitive capability.

Introduction

Familiarizing and understanding the concept behind the reasoning of human beings is a controversial issue. The language used and other stylistic aspects of sound complicate the issue even more.  At the same time, the code of conduct amongst human beings is a key determinant in predicting the corresponding successes and failures. In the past decade, researchers and psychologists have tried to justify and explain the concept behind capturing information and processing it to the real world (Vergauwe, Barrouillet & Camos, 2010). However, defining the extent to which an individual capture certain ideas and process them has raised social concerns. The researchers have not managed to define these processes and thus it calls for statistical analysis of dual tasks.

Furthermore, healthcare workers frequently come across dual task situations where intervention and assisting the public must be considered. For example, people with neurological disorders require maximum support by the medical practitioners. On the other hand, developing treatment and intervention techniques must be prioritized. In this case, nursing researchers collaborate with multidisciplinary healthcare staff to develop long term solutions while considering dual tasking (Strayer & Johnston, 2001). Another good example of dual task is the situation of a person diagnosed with Alzheimer complications. The best way to assist this person is by developing a dual task training program. Nevertheless, if an individual experienced a brain damage condition, coming up with clinical intervention with regard to mental dimension, dual task would also assist in ambulation. One of the most common views amongst scientists is that humans understanding and reasoning of language is governed by the available resources.

Hypothesis for this study.

The most appropriate hypotheses for this particular study is as follows

Hypothesis 1

Null hypothesis, Ho: visual and verbal activities are not associated with dual task situations.

Alternative hypothesis, Ha: visual and verbal activities are associated with dual task situations.

Hypothesis 2

Ho: visual and verbal activities cannot affect each other during dual tasking perspective

Ha: visual and verbal activities can affect each other during dual tasking perspective.

All the above hypothesis were used to develop and model our topic on dual tasking which aimed at establishing and explaining the concept of dual tasking. Moreover, to achieve the objectives the cognitive loading capacity of the subjects was examined and experimented with the ability to process any idea. This was also used to come up with a 4 computer system generated dual tasks. Besides, it was assumed that the nature of information was basically monotonic and undetermined. This implied that the nature of the data was based on dual training and other relevant aspects.

Methodology and Design

The participants of this study.

Sixteen participants at college level Michigan were randomly selected for this particular study. 8 of them were male and the rest were female (Strayer & Johnston, 2001). Each one of them was assigned the 4 dual tasking conditions. At the same time, the visual and verbal conditions were measure as either short, long or even medium

Procedure

In this particular study, the experiment was divided into three phases. The first one took approximately 6 minutes and the participants warmed up within a period of 3 minutes before taking part in the second one. The experiment involved listening to speech by a famous politician within the state. Phone conversations were used to track the ability and understanding of the information amongst the various participants. The control group was taken to be of 4 participants and the intervention incorporated the remaining 12. The data was then recorded for further analysis. Additionally, the independent variable was visual recall while the dependent variables were both verbal and visual spatial criteria’s respectively. The following table shows data that was collected for analysis.

To add on this, the data was then cleaned and put into a form in which carrying out analysis would be easier. The table below shows some part of the data used

  Short Long
Big 1.00 0.00
small 0.34 0.66

 

Results obtained after Carrying out Analysis

The means obtained for the verbal and visual spatial data were 86 and 82% respectively. The performance on the major task in the experiment was much lower than when put together with cognitive ability and verbal experimentation. In general, the results from the experiment indicated that both verbal and visual spatial aspects (Bourke, 1997) have an effect on the cognitive ability to recall. However, during the experiment two participants in the intervention group withdrew from the study. These subjects had an average performance of 75%. On the other hand, visual spatial and the cognitive recalling function were analyzed as either short, long or average.

Descriptive Statistics.

The following table shows the descriptive statistics for the four dual tasks carried out in our analysis.

 

Here, the descriptive statistics for our case study were basically the means as shown in the table above.

Analysis of the effects of stimulus duration and cognitive capabilities

Dependent variables

Verbal Capability: Tracking Error

After carrying out ANOVA test for the verbal capability, the following table shows the results obtained by the tracking error.

Clearly, the main effect variable was insignificant at 5% level of significance. This is because the p value was higher than 0.5 (0.1048>0.05). The f value was given as 2.9565 with 16 degrees of freedom. This implies that the mean change score in the control group was much higher than the intervention group.

Visual spatial variable: Hit Rate

The results from the above table clearly that the hit rate for this particular study was insignificant at 5% level of significance. That is 0.6895>0.05. The f value was given as 0.55478 with 16 degrees of freedom. This implies that the mean change score in the control group was much higher than the intervention group.

The Independent Variable

The results indicated that the alarm rate was significant in terms of the cognitive dimension. This is because the p value, 0.04 was found to be less than 0.05. The f value was given to be 0.080723 for the overall model. The two way analysis of variance indicated that for the alarm rate, the mean change score in the control group was much lower than the intervention group (Bourke, 1997). From the results, we reject the null hypothesis and conclude that the cognitive ability of person is affected by verbal and visual spatial dimensions.

 

Graphs for the data

The following shows graphs obtained after analyzing the DVs and IDVs.

False alarm rate

Hit Rate

 

 

Mean Tracking Error

Conclusion

Our study has revealed the relationship between verbal and visual spatial dimensions in psychology. All these affect the cognitive ability of a person. From the results, we rejected the null hypothesis and conclude that indeed a relationship between these variables. However, the study was limited because we had a small sample size. Therefore, increasing the sample size would improve the reliability and validity of our experiment. At the same time, the study can be used by other researchers and improved to yield better results. Researchers should consider incorporating other factors that can affect the cognitive ability of an individual. As a result clinical practice will be improved and thus a better future for the public at large.

 

 

References

Vergauwe, E., Barrouillet, P., & Camos, V. (2010). Do Mental Processes Share a Domain-General Resource?. Psychological Science21(3), 384-390. doi: 10.1177/0956797610361340

Bourke, P. (1997). Measuring Attentional Demand in Continuous Dual-task Performance. The Quarterly Journal Of Experimental Psychology A50(4), 821-840. doi: 10.1080/027249897391900

Strayer, D., & Johnston, W. (2001). Driven to Distraction: Dual-Task Studies of Simulated Driving and Conversing on a Cellular Telephone. Psychological Science12(6), 462-466. doi: 10.1111/1467-9280.00386

education- importance of digital technology

  1. Planning the activity (1000 words)

 

Name of the activity Bee-Spell me!

 

Year Level Year 1

 

Identified Learning Area

o   E.g. History, English, Arts

 

English and Digital Technologies
Student prior understandings

o   How much experience have students already had this technology?

o   Are they beginners, have they used this technology before, etc.

 

·         Students are aware of the program on the Bee-Bots.

·         Students have previously used it before on an alphabet mat and completely understand the button functions and its purpose.

·         Additionally, Intensive prior knowledge of the context of this activity (the story of “the dress-up box”)

Learning objectives

o   What do you want the students to achieve?

o   E.g. For the students to ……….

 

The main objective of this activity is to get students to understand that a computer (Bee-Bot) will follow precise commands and will respond to those commands.  Additionally, students will be able to effectively implement simple programming to practice new vocabulary introduced in “The Dress-up Box” story.

 

Connection to the Australian Curriculum

o   Include one descriptor from your learning area and one from the technologies

o   Copy and paste the descriptors (no reference needed)

 

Digital Technologies:

Follow, describe and represent a sequence of steps and decisions (algorithms) needed to solve simple problems (ACTDIP004) – Elaboration: writing and entering a simple set of instructions jointly to sequence events and instructions,

 

English:

Use comprehension strategies to build literal and inferred meaning about key events, ideas and information in texts that they listen to, view and read by drawing on growing knowledge of context, text structures and language features (ACELY1660) – Elaboration: building knowledge about the topic of the text and learning new vocabulary before, during and after reading

 

 

 

Explanation of activity

o   What will you do?

o   What will the students be doing?

 

Introduction of activity:

·         This activity is based on a story of which students have read on multiple occasions “The Dress-up Box”.

·         This activity consists of students trying to spell the new vocabulary they are exposed to from the story.

 

Equipment:

·         Ideally, students will be working in pairs then in groups.

Therefore, there is a need for:

·         Multiple of Bee-bots

·         Multiple of alphabet mats

·         Student’s workbook

·         Pencils

 

Teacher:

·         At the beginning of class, the teacher will demonstrate the task with the Bee-Bot as a whole class demonstration.

·         Students will be gathered around the teacher on the floor as the teacher briefly remind students of the program and functions of the Bee-Bot

·         The teacher will write a few vocabs from the storybook on the board for students to start spelling on the alphabet mat.

For example:

·         Frog

·         Bird

·         Horse

·         Pig

·         Ant

·         Box

·         Work

·         Game

·         Truck

 

·         As students begin the activity, the teacher will begin walking around to ensure proper use of the Bee-Bot is implemented.

·         The teacher will also incorporate open-ended and closed-ended questions as they walk around.

·         “What would you do to the Bee-bot now?”

·         “How many spaces do we need to make it move forward/backward etc.?”

·         “What button should I press now?”

 

 

 

Students:

·         Students will be working in pairs, taking turns in spelling different vocabulary on the alphabet mat.

·         Students will be working collaboratively to ensure they code the Bee-bot appropriately to correctly spell the word.

·         Students will be looking through the alphabet mat to find the correct letters that spell out their chosen words correctly.

·         Then, students will write down the code/pattern in their workbooks before they begin implementing it on the Bee-Bot

·         Students are welcome to choose other vocabularies from the storybook

·         *** an extension for students, is to use longer words for a harder establishment of a Bee-Bot code***

For example:

·         Haunted

·         Favorite

·         Marshmallows

·         Chocolates

 

Pedagogical choices including

o   Strategies you have chosen to engage students

o   Theories you have drawn from using evidence from the literature (refs needed)

o   Base it of these:

o   Technological knowledge

o   Pedagogical knowledge

o   Contextual knowledge

 

Some examples (you do not have to use those, use better ideas if you can):

Collaborative learning

Hands-on

Digital incorporated

Repetition of vocabs

Giving students the choice

Importance to provide an extension for students

 

It is also important to note that some pedagogy strategies are teacher-centred and relies on the methods such as a whole-class lecture, rote memorisation as well as chorus answers (Learning Portal, 2018).

 

Another strategy that can be used is the learner-centred pedagogy which indicate that the learner should take an active role in the learning process. The teacher only facilitates the learning process (Learning Portal, 2018).

 

The final strategy is the learning-centred pedagogy which borrows from both the teacher-centred and the learner centred pedagogy upon consideration of the number of students in the classroom, the physical environment, learning materials as well as the availability of teaching (Learning Portal, 2018)

 

In addition, the unit also provided an opportunity to learn on how technology may be used in the learning process which assisted to build pattern recognition which can be used in the learning of new and future technologies.

 

Also, there was also involvement with pedagogical approaches such as constructivism, behaviourism, constructionism, inquiry based learning, play based learning and collaborative learning (Learning Portal, 2018).

 

The constructivism approach submits that the human learning process is constructed in that it builds over the previous lessons. This tells us that the new learning is based on the previous knowledge (Phillips, 1995). As such there is need to appreciate what the students have learned in the past so as to come up with strategies that help the child to develop their knowledge.

The rationale for the technology (MAJOR, IMPORTANT PART)

o   Affordances of the technology

o   Why this technology is a good choice for the curriculum area

o   Why is it better than other technologies?

o   Find evidence from the literature that this technology improves learning

o   Other technologies you considered and why did you choose this technology?

o   TPACK KNOWLEDGE

 

Affordances:

 

A bee-bot is a good investment, at a cost of under $100, it brings endless learning potential to the students.

Why it is a good choice:

 

It is exciting and intriguing to the child as they are used to play online games and the learning process here mimics the games that the children usually play (LFC, 2016).

It allows children to communicate and interact as they share ideas to solve a learning problem in their pairs (Kaur, n.d.)

Children also learn with their peers without realizing it as the learning process happens in a way that is almost as fun as playing. (Smith, 2012).

 

Why is it better than other technologies:

 

It gives the students learning opportunities in the early years i.e. literacy, math, developing social and communication skills.

Working in groups also fosters collaboration (Victoria State Government, 2015).

 

Other technologies considered:

·         Flashcards

·         Videos

·         Audio presentation

 

The bee-bot was chosen as it gives the children more engagement and allows them to share ideas and collectively come up with solutions to the challenges that they may be presented with by this technology.

 

It is also important to note here that there is a need to take the Technological Pedagogical Content Knowledge (TPACK) into consideration as it identifies the nature of the knowledge that the teacher is required to have for the integration of technology into their teaching (Koehler, TPACK Explained, 2012).

 

Following TPACK, the teacher is expected to have three facets of knowledge which include Content Knowledge, Technological Knowledge, and Pedagogy Knowledge. The three are not expected to exist in isolation but to intersect such that the teacher has a full appreciation of how they can all be manipulated for the benefit of the student.

 

Content knowledge here refers to the teacher being in the know of what needs to be taught (Koehler & Mishra, What is technological pedagogical content knowledge?, 2009). Here the teacher researches on what topics that will be covered in class in order to have the right content for the class. This is also supported with the curriculum that will be adopted.

 

On the other hand, the Pedagogical Knowledge pertains to how they teach can transform the subject matter for teaching in a way that will be understood and appreciated by the student which stems from the teachers deep understanding of how students and the skills that are required to manage the classroom (Kurt, 2016). This pertains to the choosing a way of imparting knowledge to the children that will best suit the leaners. For instance, the Bee-bot is a way that was chosen to impart knowledge to the children which works well with their age and the content that is being learned.

 

 

Finally, technology knowledge pertains to the knowledge of how to work with certain technologies in the delivery of lessons to the audience (TTF, 2020). In this case, the Bee-bot is a technological intervention that was used to help the children learn Math and the Alphabet.

 

Moreover, TPACK also looks at the implementation of pedagogical strategies in the exercising of duties by the educator community. These strategies are aimed at enabling teachers to influence the learning process of students to capture their attention (Edsys, 2020). Strategies that may be employed include pedagogical creativity which includes the use of creative tools such as brainstorming techniques, the concept of learning outside the classroom as well as storyboarding among others.

 

.

 

Assessment

o   How will you evaluate if the learning objectives have been met?

o   Formative and Summative

o   Informal or formal

 

Some examples: (you do not have to use those, use better ideas if you can):

 

Evaluation:

Teacher observation

Peer observation/assessment (keeping each other accountable)

Students ability to demonstrate the fundamental movements that they need, to make the bee-bot perform the task

Formative assessment:

The teacher will have the class taught lessons progressively. After each lesson, the teacher will assess whether the students have captured the lesson adequately (Weaver, 2020)

Summative assessment:

The teacher will test student knowledge about a previously learned concept such as addition, subtraction, and the alphabet.

Students ability to correctly spell out a word using the alphabet mat

Informal assessment:

The teacher will look at the performance of the children on how they are learning from the bee-bot and records of progress will be kept on file for review.

Formal assessment:

The teacher will test the students on the activities that can be learned with the bee-bot to have a record of the actual performance of each child.

 

 

Safety and ethical considerations

o   Physical

o   Digital

 

Physical safety

The teacher will ensure the safe use of the bee-bot by moving around the classroom to ensure that the children are using it correctly (TTS, 2020).

Digital safety

The Bee-bot has little or no digital risk

Practical considerations

o   Timing/setup

o   Cost

 

Timing/setup

The teacher will set up the Bee-bot to make it ready for use by the children

Cost

The bee-bot is current costing up to 49pounds on eBay

 

References

Edsys. (2020). 15 best Pedagogical Strategies for innovative learning. Retrieved from edsys.in: https://www.edsys.in/best-pedagogical-strategies/

Kaur, K. (n.d.). Benefits of bee bots in classroom. Retrieved from weebly.com: http://beebotsed.weebly.com/benefits-of-bee-bots-in-classrooms.html

Koehler, M. (2012, September 12). TPACK Explained. Retrieved from matt-koehler.com: http://matt-koehler.com/tpack2/tpack-explained/

Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 60-70.

Kurt, S. (2016, September 16). TPACK: Technological Pedagogical Content Knowledge Framework. Retrieved from educationaltechnology.net: https://educationaltechnology.net/technological-pedagogical-content-knowledge-tpack-framework/

Learning Portal. (2018, March 29). Effective and appropriate pedagogy. Retrieved from learningportal.iiep.unesco.org: https://learningportal.iiep.unesco.org/en/issue-briefs/improve-learning/teachers-and-pedagogy/effective-and-appropriate-pedagogy

LFC. (2016). Bee-bot and accessories. Retrieved from lfccatalogue.co.uk: http://www.lfccatalogue.co.uk/bee-bot-and-accessories.html

Phillips, D. (1995). The good, the bad, and the ugly: The many faces of constructivism. Educational researcher, 5-12.

Smith, T. (2012, May 31). Bee-Bot Reviews. Retrieved from robotcenter.co.uk: https://www.robotcenter.co.uk/pages/bee-bot-reviews

TTF. (2020). Technological Pedagogical Content Knowledge (TPACK). Retrieved from ttf.edu.au: https://ttf.edu.au/what-is-tpack/what-is-tpack.html

TTS. (2020). Bee-Bot – a teacher’s guide. Retrieved from tts-group.co.uk: https://www.tts-group.co.uk/blog/2018/07/18/bee-bot-a-teachers-guide.html

Victoria State Government. (2015). The e5 instructional model. Retrieved from education.vic.gov.au: http://www.education.vic.gov.au/school/teachers/support/pages/e5.aspx

Weaver, B. (2020). Formal vs. Informal Assessments. Retrieved from scholastic.com: https://www.scholastic.com/teachers/articles/teaching-content/formal-vs-informal-assessments/

Zouaoui, M. (2017, April 1). 5 Technology Integration Plan Steps You Can Tell Your Principal About. Retrieved from elearningindustry.com: https://elearningindustry.com/5-technology-integration-plan-steps-can-tell-principal

 

 

 

  1. Example of learning activity (if this is a video, please save it in Google Drive, share it to anyone at Monash with the link, and post the link here.)

Leave this one for me to do and record the activity.

 

  1. Unit reflection

 

 

The adoption of technology in education continues to disrupt the educational sector aiding the teachers and educators to provide better deliverables. It is important to note here that teaching strategies that use technology are deemed to be ethical as they facilitate the students to learn, boost their capacity as well as their productivity and capacity. It has been noted that the integration of technology in education greatly inspires positive changes in teaching methods. The impact that this unit had on my learning includes that I can adopt technology in the learning process as it has the advantage of making teaching easy while it also positively impacts the inclination towards learning as one can be learning without knowing and it and can enjoy it as much as they enjoy playing.

I also liked the fact that through technology, it is easier to track the progress that students will be making through various assessments that include formative and informative assessment methods (Zouaoui, 2017). Another thing that I liked about the unit is that with the young students addicted to social media, games, and television, the introduction of e-learning to the classroom makes learning more exciting to the students.

In view thereof, I think that technology should be introduced into the education sector as it makes collaborative more effective not only amongst students but also between the teacher and the students. It is also important to indicate that technology has been observed to do away with any limitations as learning through technology assists the student to use more ways to learn rather than the traditional books. Technology has also made the learning experience more hands-on. Moreover, technology also makes the testing of students done in real-time and hence the assessment of students is done instantly, and results on tests can also be produced instantly with technology.

The challenges that I faced include that I was not conversant with the technology initiatives that were being introduced as methods of ensuring learning process amongst students. However, continuous use of these technological platforms made me more comfortable with time until I was comfortable to use the platforms. Also, it took me a considerable amount of time to learn how to make use of the Bee-bot and later teach the students how to use it for their practical lessons.

This has also helped me to gain the contextual aspect of the technological and pedagogical and how that impacts on the type of technology that may be used and how to use it in helping students learn. This has showed how the teachers can consider the various technological options, the barriers that may find them not being able to adopt technological ways of imparting knowledge to students and hw the lack of teacher confidence can result in lack of explicit instruction.

I think I will be able to adopt the online learning strategy and teach my students online as it gives me more potential to engage with the students since they are already interested in staying online. This would be more interesting as the students will be more willing to collaborate with the teacher and learn more in the process.

 

 

Conclusion

To conclude, the unit has highlighted the importance of technology in the classroom as it has proved to have several advantages that will benefit both the teacher and the student; this includes easy impartation of knowledge through collaboration and immediate feedback. More importantly, it was also noted that with technology, students will enjoy learning as much as they enjoy playing which keeps them engaged in the learning process for longer than where there is no technology involved. Also, the students will be able to share ideas and collaborate more with their teachers.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

education- importance of digital technology

  1. Planning the activity (1000 words)

 

Name of the activity Bee-Spell me!

 

Year Level Year 1

 

Identified Learning Area

o   E.g. History, English, Arts

 

English and Digital Technologies
Student prior understandings

o   How much experience have students already had this technology?

o   Are they beginners, have they used this technology before, etc.

 

·         Students are aware of the program on the Bee-Bots.

·         Students have previously used it before on an alphabet mat and completely understand the button functions and its purpose.

·         Additionally, Intensive prior knowledge of the context of this activity (the story of “the dress-up box”)

Learning objectives

o   What do you want the students to achieve?

o   E.g. For the students to ……….

 

The main objective of this activity is to get students to understand that a computer (Bee-Bot) will follow precise commands and will respond to those commands.  Additionally, students will be able to effectively implement simple programming to practice new vocabulary introduced in “The Dress-up Box” story.

 

Connection to the Australian Curriculum

o   Include one descriptor from your learning area and one from the technologies

o   Copy and paste the descriptors (no reference needed)

 

Digital Technologies:

Follow, describe and represent a sequence of steps and decisions (algorithms) needed to solve simple problems (ACTDIP004) – Elaboration: writing and entering a simple set of instructions jointly to sequence events and instructions,

 

English:

Use comprehension strategies to build literal and inferred meaning about key events, ideas and information in texts that they listen to, view and read by drawing on growing knowledge of context, text structures and language features (ACELY1660) – Elaboration: building knowledge about the topic of the text and learning new vocabulary before, during and after reading

 

 

 

Explanation of activity

o   What will you do?

o   What will the students be doing?

 

Introduction of activity:

·         This activity is based on a story of which students have read on multiple occasions “The Dress-up Box”.

·         This activity consists of students trying to spell the new vocabulary they are exposed to from the story.

 

Equipment:

·         Ideally, students will be working in pairs then in groups.

Therefore, there is a need for:

·         Multiple of Bee-bots

·         Multiple of alphabet mats

·         Student’s workbook

·         Pencils

 

Teacher:

·         At the beginning of class, the teacher will demonstrate the task with the Bee-Bot as a whole class demonstration.

·         Students will be gathered around the teacher on the floor as the teacher briefly remind students of the program and functions of the Bee-Bot

·         The teacher will write a few vocabs from the storybook on the board for students to start spelling on the alphabet mat.

For example:

·         Frog

·         Bird

·         Horse

·         Pig

·         Ant

·         Box

·         Work

·         Game

·         Truck

 

·         As students begin the activity, the teacher will begin walking around to ensure proper use of the Bee-Bot is implemented.

·         The teacher will also incorporate open-ended and closed-ended questions as they walk around.

·         “What would you do to the Bee-bot now?”

·         “How many spaces do we need to make it move forward/backward etc.?”

·         “What button should I press now?”

 

 

 

Students:

·         Students will be working in pairs, taking turns in spelling different vocabulary on the alphabet mat.

·         Students will be working collaboratively to ensure they code the Bee-bot appropriately to correctly spell the word.

·         Students will be looking through the alphabet mat to find the correct letters that spell out their chosen words correctly.

·         Then, students will write down the code/pattern in their workbooks before they begin implementing it on the Bee-Bot

·         Students are welcome to choose other vocabularies from the storybook

·         *** an extension for students, is to use longer words for a harder establishment of a Bee-Bot code***

For example:

·         Haunted

·         Favorite

·         Marshmallows

·         Chocolates

 

Pedagogical choices including

o   Strategies you have chosen to engage students

o   Theories you have drawn from using evidence from the literature (refs needed)

o   Base it of these:

o   Technological knowledge

o   Pedagogical knowledge

o   Contextual knowledge

 

Some examples (you do not have to use those, use better ideas if you can):

Collaborative learning

Hands-on

Digital incorporated

Repetition of vocabs

Giving students the choice

Importance to provide an extension for students

 

The rationale for the technology (MAJOR, IMPORTANT PART)

o   Affordances of the technology

o   Why this technology is a good choice for the curriculum area

o   Why is it better than other technologies?

o   Find evidence from the literature that this technology improves learning

o   Other technologies you considered and why did you choose this technology?

o   TPACK KNOWLEDGE

 

Affordances:

 

A bee-bot is a good investment, at a cost of under $100, it brings endless learning potential to the students.

Why it is a good choice:

 

It is exciting and intriguing to the child (LFC, 2016).

It allows children to communicate and interact (Kaur, n.d.)

Children also learn with their peers without realizing it (Smith, 2012).

 

Why is it better than other technologies:

 

It gives the students learning opportunities in the early years i.e. literacy, math, developing social and communication skills.

Working in groups also fosters collaboration (Victoria State Government, 2015).

 

Other technologies considered:

·         Flashcards

·         Videos

·         Audio presentation

 

The bee-bot was chosen as it gives the children more engagement and allows them to share ideas and collectively come up with solutions to the challenges that they may be presented with by this technology.

 

It is also important to note here that there is a need to take the Technological Pedagogical Content Knowledge (TPACK) into consideration as it identifies the nature of the knowledge that the teacher is required to have for the integration of technology into their teaching (Koehler, TPACK Explained, 2012).

 

Following TPACK, the teacher is expected to have three facets of knowledge which include Content Knowledge, Technological Knowledge, and Pedagogy Knowledge. The three are not expected to exist in isolation but to intersect such that the teacher has a full appreciation of how they can all be manipulated for the benefit of the student.

 

Content knowledge here refers to the teacher being in the know of what needs to be taught (Koehler & Mishra, What is technological pedagogical content knowledge? 2009). On the other hand, the Pedagogical Knowledge pertains to how they teach can transform the subject matter for teaching in a way that will be understood and appreciated by the student which stems from the teachers deep understanding of how students and the skills that are required to manage the classroom (Kurt, 2016). Finally, technology knowledge pertains to the knowledge of how to work with certain technologies in the delivery of lessons to the audience (TTF, 2020).

 

Moreover, TPACK also looks at the implementation of pedagogical strategies in the exercising of duties by the educator community. These strategies are aimed at enabling teachers to influence the learning process of students to capture their attention (Edsys, 2020). Strategies that may be employed include pedagogical creativity which includes the use of creative tools such as brainstorming techniques, the concept of learning outside the classroom as well as storyboarding among others.

 

Assessment

o   How will you evaluate if the learning objectives have been met?

o   Formative and Summative

o   Informal or formal

 

Some examples: (you do not have to use those, use better ideas if you can):

 

Evaluation:

Teacher observation

Peer observation/assessment (keeping each other accountable)

Students ability to demonstrate the fundamental movements that they need, to make the bee-bot perform the task

Formative assessment:

The teacher will have the class taught lessons progressively. After each lesson, the teacher will assess whether the students have captured the lesson adequately (Weaver, 2020)

Summative assessment:

The teacher will test student knowledge about a previously learned concept such as addition, subtraction, and the alphabet.

Students ability to correctly spell out a word using the alphabet mat

Informal assessment:

The teacher will look at the performance of the children on how they are learning from the bee-bot and records of progress will be kept on file for review.

Formal assessment:

The teacher will test the students on the activities that can be learned with the bee-bot to have a record of the actual performance of each child.

 

 

Safety and ethical considerations

o   Physical

o   Digital

 

Physical safety

The teacher will ensure the safe use of the bee-bot by moving around the classroom to ensure that the children are using it correctly (TTS, 2020).

Digital safety

The Bee-bot has little or no digital risk

Practical considerations

o   Timing/setup

o   Cost

 

Timing/setup

The teacher will set up the Bee-bot to make it ready for use by the children

Cost

The bee-bot is current costing up to 49pounds on eBay

 

References

Edsys. (2020). 15 best Pedagogical Strategies for innovative learning. Retrieved from edsys.in: https://www.edsys.in/best-pedagogical-strategies/

Kaur, K. (n.d.). Benefits of bee bots in the classroom. Retrieved from weebly.com: http://beebotsed.weebly.com/benefits-of-bee-bots-in-classrooms.html

Koehler, M. (2012, September 12). TPACK Explained. Retrieved from matt-koehler.com: http://matt-koehler.com/tpack2/tpack-explained/

Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 60-70.

Kurt, S. (2016, September 16). TPACK: Technological Pedagogical Content Knowledge Framework. Retrieved from educationaltechnology.net: https://educationaltechnology.net/technological-pedagogical-content-knowledge-tpack-framework/

LFC. (2016). Bee-bot and accessories. Retrieved from lfccatalogue.co.uk: http://www.lfccatalogue.co.uk/bee-bot-and-accessories.html

Smith, T. (2012, May 31). Bee-Bot Reviews. Retrieved from robotcenter.co.uk: https://www.robotcenter.co.uk/pages/bee-bot-reviews

TTF. (2020). Technological Pedagogical Content Knowledge (TPACK). Retrieved from ttf.edu.au: https://ttf.edu.au/what-is-tpack/what-is-tpack.html

TTS. (2020). Bee-Bot – a teacher’s guide. Retrieved from tts-group.co.uk: https://www.tts-group.co.uk/blog/2018/07/18/bee-bot-a-teachers-guide.html

Victoria State Government. (2015). The e5 instructional model. Retrieved from education.vic.gov.au: http://www.education.vic.gov.au/school/teachers/support/pages/e5.aspx

Weaver, B. (2020). Formal vs. Informal Assessments. Retrieved from scholastic.com: https://www.scholastic.com/teachers/articles/teaching-content/formal-vs-informal-assessments/

Zouaoui, M. (2017, April 1). 5 Technology Integration Plan Steps You Can Tell Your Principal About. Retrieved from elearningindustry.com: https://elearningindustry.com/5-technology-integration-plan-steps-can-tell-principal

 

 

 

  1. Example of learning activity (if this is a video, please save it in Google Drive, share it to anyone at Monash with the link, and post the link here.)

Leave this one for me to do and record the activity.

 

  1. Unit reflection

 

 

The adoption of technology in education continues to disrupt the educational sector aiding the teachers and educators to provide better deliverables. It is important to note here that teaching strategies that use technology are deemed to be ethical as they facilitate the students to learn, boost their capacity as well as their productivity and capacity. It has been noted that the integration of technology in education greatly inspires positive changes in teaching methods. The impact that this unit had on my learning includes that I can adopt technology in the learning process as it has the advantage of making teaching easy while it also positively impacts the inclination towards learning as one can be learning without knowing and it and can enjoy it as much as they enjoy playing.

I also liked the fact that through technology, it is easier to track the progress that students will be making through various assessments that include formative and informative assessment methods (Zouaoui, 2017). Another thing that I liked about the unit is that with the young students addicted to social media, games, and television, the introduction of e-learning to the classroom makes learning more exciting to the students.

In view thereof, I think that technology should be introduced into the education sector as it makes collaborative more effective not only amongst students but also between the teacher and the students. It is also important to indicate that technology has been observed to do away with any limitations as learning through technology assists the student to use more ways to learn rather than the traditional books. Technology has also made the learning experience more hands-on. Moreover, technology also makes the testing of students done in real-time and hence the assessment of students is done instantly, and results on tests can also be produced instantly with technology.

I think I will be able to adopt the online learning strategy and teach my students online as it gives me more potential to engage with the students since they are already interested in staying online. This would be more interesting as the students will be more willing to collaborate with the teacher and learn more in the process.

 

 

Conclusion

To conclude, the unit has highlighted the importance of technology in the classroom as it has proved to have several advantages that will benefit both the teacher and the student; this includes easy impartation of knowledge through collaboration and immediate feedback. More importantly, it was also noted that with technology, students will enjoy learning as much as they enjoy playing which keeps them engaged in the learning process for longer than where there is no technology involved. Also, the students will be able to share ideas and collaborate more with their teachers.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

education- importance of digital technology

  1. Planning the activity (1000 words)

 

Name of the activity Bee-Spell me!

 

Year Level Year 1

 

Identified Learning Area

o   E.g. History, English, Arts

 

English and Digital Technologies
Student prior understandings

o   How much experience have students already had this technology?

o   Are they beginners, have they used this technology before etc.

 

·         Students are aware of the program on the Bee-Bots.

·         Students have previously used it before on an alphabet mat and completely understand the button functions and its purpose.

·         Additionally, Intensive prior knowledge of the context of this activity (the story of “the dress up box”)

Learning objectives

o   What do you want the students to achieve?

o   E.g. For the students to ……….

 

The main objective of this activity is to get students to understand that a computer (Bee-Bot) will follow precise commands and will respond to those commands.  Additionally, students will be able to effectively implement simple programming to practice new vocabulary introduced in “The Dress up Box” story.

 

Connection to the Australian Curriculum

o   Include one descriptor from your learning area and one from the technologies

o   Copy and paste the descriptors (no reference needed)

 

Digital Technologies:

Follow, describe and represent a sequence of steps and decisions (algorithms) needed to solve simple problems (ACTDIP004) – Elaboration: writing and entering a simple set of instructions jointly to sequence events and instructions,

 

English:

Use comprehension strategies to build literal and inferred meaning about key events, ideas and information in texts that they listen to, view and read by drawing on growing knowledge of context, text structures and language features (ACELY1660) – Elaboration: building knowledge about the topic of the text and learning new vocabulary before, during and after reading

 

 

 

Explanation of activity

o   What will you do?

o   What will the students be doing?

 

Introduction of activity:

·         This activity is based off a story of which students have read on multiple occasions “The Dress up Box”.

·         This activity consists of students trying to spell the new vocabulary they are exposed to from the story.

 

Equipment:

·         Ideally, students will be working in pairs than in groups.

Therefore, there is a need for:

·         Multiple of Bee-bots

·         Multiple of alphabet mats

·         Student’s workbook

·         Pencils

 

Teacher:

·         In the beginning of class, the teacher will demonstrate the task with the Bee-Bot as a whole class demonstration.

·         Students will be gathered around the teacher on the floor as the teacher briefly remind students of the program and functions of the Bee-Bot

·         Teacher will write a few vocabs from the storybook on the board for students to start spelling on the alphabet mat.

For example:

·         Frog

·         Bird

·         Horse

·         Pig

·         Ant

·         Box

·         Work

·         Game

·         Truck

 

·         As students begin the activity, the teacher will begin walking around to ensure proper use of the Bee-Bot is implemented.

·         The teacher will also incorporate open ended and closed ended questions as they walk around.

·         “What would you do to the Bee-bot now?”

·         “How many spaces do we need to make it move forward/backward etc.?”

·         “What button should I press now?”

 

 

 

Students:

·         Students will be working in pairs, taking turn in spelling different vocabulary on the alphabet mat.

·         Students will be working collaboratively to ensure they code the Bee-bot appropriately to correctly spell the word.

·         Students will be looking through the alphabet mat to find the correct letters that spell out their chosen word correctly.

·         Then, students will write down the code/pattern in their workbooks before they begin implementing it on the Bee-Bot

·         Students are welcome to choose other vocabularies from the storybook

·         *** an extension for students, is to use longer words for a harder establishment of a Bee-Bot code***

For example:

·         Haunted

·         Favourite

·         Marshmallows

·         Chocolates

 

Pedagogical choices including

o   Strategies you have chosen to engage students

o   Theories you have drawn from using evidence from the literature (refs needed)

o   Base it of these:

o   Technological knowledge

o   Pedagogical knowledge

o   Contextual knowledge

 

Some examples (you do not have to use those, use better ideas if you can):

Collaborative learning

Hands on

Digital incorporated

Repetition of vocabs

Giving students the choice

Importance to provide an extension for students

 

Rationale for the technology (MAJOR, IMPORTANT PART)

o   Affordances of the technology

o   Why this technology is a good choice for the curriculum area

o   Why is it better than other technologies?

o   Find evidence from the literature that this technology improves learning

o   Other technologies you considered and why did you choose this technology?

o   TPACK KNOWLEDGE

 

Affordances:

 

A bee-bot is a good investment, at a cost of under $100, it brings endless learning potential to the students.

Why it is a good choice:

 

It is exciting and intriguing to the child

Allows children to communicate and interact

Children also learn with their peers without realising it.

 

Why is it better than other technologies:

 

It gives the students learning opportunities in the early years i.e. literacy, math, developing social and communication skills.

Working in groups also fosters collaboration

 

Other technologies considered:

·         Flash cards

·         Videos

·         Audio presentation

The bee-bot was chosen as it gives the children more engagement and allows them to share ideas and collectively come up with solutions to the challenges that they may be presented with by this technology.

 

 

Assessment

o   How will you evaluate if the learning objectives have been met?

o   Formative and Summative

o   Informal or formal

 

Some examples: (you do not have to use those, use better ideas if you can):

 

Evaluation:

Teacher observation

Peer observation/assessment (keeping each other accountable)

Students ability to demonstrate the fundamental movements that they need, to make the bee-bot perform the task

Formative assessment:

The teacher will have the class taught lessons progressively. After each lesson, the teacher will assess whether the students have captured the lesson adequately

Summative assessment:

The teacher will test student knowledge about a previously learned concept such as addition, subtraction and the alphabet.

Students ability to correctly spell out a word using the alphabet mat

Informal assessment:

The teacher will look at the performance of the children on how they are learning from the bee-bot and records of progress will be kept on file fore review.

Formal assessment:

The teacher will test the students on the activities that can be learnt with the bee-bot to have a record of actual performance of each child.

 

 

Safety and ethical considerations

o   Physical

o   Digital

 

Physical safety

The teacher will ensure safe use of the bee-bot by moving around the classroom to ensure that the children are using it correctly.

Digital safety

The Bee-bot has little or no digital risk

Practical considerations

o   Timing/setup

o   Cost

 

Timing/setup

The teacher will set up the Bee-bot to make it ready for use by the children

Cost

The bee-bot is current costing up to 49pounds on eBay

 

Reference list

 

 

  1. Example of learning activity (if this is a video, please save it in Google drive, share it to anyone at Monash with the link, and post the link here.)

Leave this one for me to do and record of the activity.

 

  1. Unit reflection

 

 

The adoption of technology in education continues to disrupt the educational sector aiding the teachers and educators to provide better deliverables. It is important to note here that teaching strategies that use technology are deemed to be ethical as they facilitate the students to learn, boost their capacity as well as their productivity and capacity. It has been noted that integration of technology in education greatly inspires positive changes in teaching methods. Below are the lessons learnt in this unit.

Technology makes teaching easy

 

 

            Technology has the advantage of making teaching easy in the place of the instructors. For instance, the bee-bot naturally makes the students want to learn as it is intriguing and exciting. As such the students will not need to be pushed to learn as they are already interested in the robot which to them appears like a toy. As such, the students are learning without knowing and it and they enjoy it as much as they enjoy play.

Technology also helps to track the progress of the students

Through technology, it is also easier to track the progress that the children will be making through various assessments that include formative and informative assessments methods.

Positive environmental impact

            Technology has also been seen to have a positive impact on the environment. The use of the Bee-bot will prevent excess use of paper and plastic which would have a negative impact on the environment.

Students enjoy learning

With the young students addicted to games and television, the introduction of the Bee-bot to the classroom makes learning more exciting to the students. While presumably playing with the gadget the students will also be learning. Additionally, while working in pairs, the students will be able to share ideas and collaborate which is also enjoyable to the students

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Viable Software Engineering techniques: Solution for Threat complexities in Secure Multiparty Computation(MPC) with Big Data

Solution for Threat Complexities in Secure Multipart Computation (MPC) With Big Data.

First A. Author, Second B. Author Jr., and Third C. Author, Member, IEEE

Abstract— The aim of this paper is to address the origins of threat complexities associated with secure multi-party computations (MPCs) and mitigation approaches that can be taken as a solution. The development of MPCs in big data analytics continues to expand and it involves private inputs from several parties who do not trust each other. Secure MPC systems allows a cryptographic protocol which ensures a correct output and security from different adversaries. Propagation of threat complexities is sometimes caused by lack of knowledge of MPC protocols and associated properties which must be considered. These mitigation approaches have been addressed in this paper; including the exploitation of static and adaptative adversaries. Recovering and re-using once-corrupted components in the MPC system has been presented as one of the solutions to adaptive adversaries. The ultimate exploitation justifies the implementation of MPCs with big data even though there are still some implications.

————————————————

·   F.A. Author is with the National Institute of Standards and Technology, Boulder, CO 80305. E-mail: author@ boulder.nist.gov.

·   S.B. Author Jr. is with the Department of Physics, Colorado State University, Fort Collins, CO 80523. E-mail: author@colostate.edu.

·   T.C. Author is with the Electrical Engineering Department, University of Colorado, Boulder, CO 80309. On leave from the National Research Institute for Metals, Tsukuba, Japan E-mail: author@nrim.go.jp.

——————————   u   ——————————

1   Introduction

 

B

ig data analytics have been a major development in modern business processes. However, to generate accurate results, companies running such analytics might need to access private data from different sources. Many companies are protective of their private data and hence, it becomes a challenge to running such processes across multiple sources. Secure multi-party computations (MPC) comes as a solution to allow joint computation across multiple parties without them disclosing or revealing their private data inputs. Secure MPC, as a cryptographic technique, is only implemented selectively on larger workflows. [1]. The real-world implementation of MPC faces the following challenges:

  • The integration of multi-party computation with data processing systems and analytics workflows is often poor.
  • Enable to run analytics in a multi-party computation framework, expert knowledge must be significant.
  • Due to MPC frameworks incapability to support data-parallel processing outside the MPC, the scaling of frameworks to large data sets is often poor.

Real-world examples of MPCs application include; Boston wage gap, Google advertising conversion, MPC for cryptographic key protection, Government Collaboration and Privacy-preserving analytics. [2].

In this work, the adversaries, challenges and applications of secure MPCs are addressed. This will help establish the viability of implementing MPCs for cases involving large data sets.

The main goal of secure MPC is to allow different data owners who might not trust each other to unite in the computation of a function which depends on their private data inputs. All of the participants involved in MPC computations are data input owners. [3].

2   Mpc Properties And Protocols

Evans, Kolesnikov, and Rosulek (2018), used a classical example of an MPC problem involving large data sets. Here, two millionaires wanted to know who was richer without knowing the net worth of each other. However, it is mandatory to first understand the basic roles of MPC systems:

  • Input Parties (IP) sending data to the private computation
  • Result Parties (RP) who get results from the private computation
  • Computing Parties (CP) who do the joint private computation.

The most common protocol in the MPC system, for each person or organization involved in these roles, is that there is no single point of trust. This means, none of the computing parties can gain access to the encrypted source data. There are several properties that the MPC protocols have, so as to enhance the security, robustness and efficiency of the MPC systems. The most important protocols are as follows:

  • n, which is the number of CPs that are to be involved in the system
  • f, the maximum number of CPs that are allowed to run the intended protocol or regulate the MPC system. f + 1 can be addressed as a violation of the system.
  • Passive security which guarantees source data privacy such that the CPs involved in the MPC system execute reliable protocols.
  • Abort active security, which ensures that the corrupt CP runs the purported protocol or else, the protocol is aborted.
  • Fault tolerance Active Security; ensures that the system continues to operate even when a CP has ceased to operate correctly.

MPC does not depend on the trustworthiness of institutions or individuals. It can, also, be used to create a neutral MPC from combining many other trusted entities. Another solution can be in combining trusted entities with those having opposing interests so as to create a more reliable MPC. Implementing the protocols discussed earlier on relevant applications will guarantee a more optimal result. [3]

3   Adversaries And Mitigation Strategies

Security of the MPC system is of vital consideration and we regard it as being secure against threat complexities if an ideal adversary simulation of a real adversary attacking the MPC protocols in an ideal world can be made. [4]. Since secure MPC’s deal with private big data computations, there are many adversaries who can end up being attracted to invading and accessing the encrypted inputs. These adversaries can be categorized as semi-honest, malicious or covert.

  • Semi-honest adversaries are called Honest-but-curious. These are honest in terms of ensuring proper execution of the MPC system to acquire an accurate evaluation, but they are also curious to revealing the private input of the participating entities.
  • Malicious adversaries violate the agreed protocol so that they manipulate the computation output or learn the private data provided by the participants.
  • Covert adversaries, on the other hand, are more like malicious adversaries except that their intention is to cheat and eventually, never caught or apprehended.

These classes of adversaries can be static or adaptive. Static adversaries will attack the computation system before it is executed. Adaptive adversaries are liable to invade parties at any stage during the computation process; this renders them more difficult to defend. The success of these adversaries is determined by many other factors such as the number of participants that they can corrupt. [5]. Adversaries are the most security concern when it comes to MPCs. Sometimes the corruption of one party involved in the computation can compromise the efficiency and security of the whole computation. If all components involved in the system are corrupted or invaded, the complexity can be difficult to solve. [6]. Adaptive adversaries maybe come through as human fraud, weaknesses in the operating system or viruses. A good way to counteract such security breaches is by recovering and reusing components of the system that may have been corrupted. Transient break-ins can be overcome using the following approach:

  • Tasks and responsibilities are branched and channeled through different components. This will allow the overall security to remain intact in case one of the parties has been invaded.
  • A mechanism for automatic recovery can be designed and used for one component with the help of other components such that automatic recovery occurs once that component is no longer corrupt.
  • Install an automatic periodic recovery system mechanism for all the components involved in the computation. [7].

Other mitigation strategies to avoid complexities on MPC systems are Statistical security and computational security. [8].

The application of secure MPC systems is not sufficient for all applications as there are some limitations. A common example of these limitations is deep learning. Other limitations are due to the availability of expertise or software frameworks. Machine learning libraries for MPCs are not readily available and there is a limited availability of equivalent systems such as R or Scikit-learn. Implementation of MPCs on big data requires much expertise ad this becomes one of its limitations. [9].

4   Important Properties To Consider

Merging MPC systems for big data processing is a major concern as there are no efficient protocols or advanced communication developments. In processing big data, there are certain paramount properties to consider and these include:

  • Exploiting random access; in order to enable a secure computation, large data sets are branched by converting a program into a circuit. This makes the option less feasible.
  • Exploiting Parallelism; this helps in solving big data problems effectively. An example is Parallel RAM which allows CPUs communicate with each other whilst accessing the same shared external memory.
  • Exploiting Plurality of Users; since large number of parties can run in parallel RAMs must be secured by balancing the load across all nodes.
  • Communication Locality; to avoid high costs in establishing communication channels involving large number of parties, the locality of communication can be minimized so that communications are transmitted simultaneously during the progression of the protocol. [10]

5   Conclusion

In today’s real-world development of business analytics, computations involving big data have attracted many adversaries whose intention is to access the private data of business entities. This has led to the development of MPCs which are implemented to compute a satisfying result without the participants being privy to the parallel private data. However, for the MPCs to be more secure, we can deduct that there is need for proper implementation of regulations or protocols by operators in the MPC system. The types of adversaries must be known so as to develop mitigation strategies that can help secure private input data. In this paper, it shows that adherence to certain protocols require awareness and expertise from the participants. Satisfying the latter means the implementation of MPCs with big data can be feasible and more secure.

Acknowledgment

The authors wish to thank A, B, C. This work was supported in part by a grant from XYZ.

References

[1] N. Volgushev, M. Schwarzkopf, A. Lapets, M. Varia and A. Bestavros, “DEMO: Integrating MPC in Big Data Workflows,” CCS’16, p. http://dx.doi.org/10.1145/2976749.2989034, 24-28 Oct 2016.
[2] Y. Lindell, “Secure Multiparty Computation (MPC),” 2020. [Online]. Available: http://www.eprint.iacr.org/2020/300.pdf.
[3] D. Evans, V. Kolesnikov and M. Rosulek, “A Pragmatic Introduction to Secure Multi-Party Computation,” Foundations and Trends® in Privacy and Security, pp. 2-3, 2018.
[4] C. Orlandi, “Is Multiparty Computation Any Good In Practice?,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 2011.
[5] J. I. Choi and K. R. B. Butler, “Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities,” Security and Communication Networks, p. 28, 2019.
[6] Y. Ishai, M. Mittal and R. Ostrovsky, “On the Message Complexity of Secure Multiparty Computation,” in IACR International Workshop on Public Key Cryptography, 2018.
[7] R. Canetti, “Studies in Secure Multiparty Computation and Applications,” Scientific Council of The Weizmann Institute of Science, 1996.
[8] A. Aly, “Network Flow Problems with Secure Multiparty Computation,” PhD Organization: Universit´e catholique de Louvain, p. 151, n.d.
[9] P. Koster, “Secure MultiParty Computation (MPC) for Big Data Analytics: technology readiness from an enterprise perspective,” November 2019. [Online]. Available: https://www.solar-project.eu.
[10] E. Boyle, K.-M. Chung and R. Pass, “Large-Scale Secure Computation: Multi-party Computation for (Parallel) RAM Programs,” Advances in Cryptology, pp. 742-762, 2015.

 

 

Viable Software Engineering techniques: Solution for Threat complexities in Secure Multiparty Computation(MPC) with Big Data

Solution for Threat Complexities in Secure Multipart Computation (MPC) With Big Data.

First A. Author, Second B. Author Jr., and Third C. Author, Member, IEEE

Abstract— The aim of this paper is to address the origins of threat complexities associated with secure multi-party computations (MPCs) and mitigation approaches that can be taken as a solution. The development of MPCs in big data analytics continues to expand and it involves private inputs from several parties who do not trust each other. Secure MPC systems allows a cryptographic protocol which ensures a correct output and security from different adversaries. Propagation of threat complexities is sometimes caused by lack of knowledge of MPC protocols and associated properties which must be considered. These mitigation approaches have been addressed in this paper; including the exploitation of static and adaptative adversaries. Recovering and re-using once-corrupted components in the MPC system has been presented as one of the solutions to adaptive adversaries. The ultimate exploitation justifies the implementation of MPCs with big data even though there are still some implications.

————————————————

·   F.A. Author is with the National Institute of Standards and Technology, Boulder, CO 80305. E-mail: author@ boulder.nist.gov.

·   S.B. Author Jr. is with the Department of Physics, Colorado State University, Fort Collins, CO 80523. E-mail: author@colostate.edu.

·   T.C. Author is with the Electrical Engineering Department, University of Colorado, Boulder, CO 80309. On leave from the National Research Institute for Metals, Tsukuba, Japan E-mail: author@nrim.go.jp.

——————————   u   ——————————

1   Introduction

 

B

ig data analytics have been a major development in modern business processes. However, to generate accurate results, companies running such analytics might need to access private data from different sources. Many companies are protective of their private data and hence, it becomes a challenge to running such processes across multiple sources. Secure multi-party computations (MPC) comes as a solution to allow joint computation across multiple parties without them disclosing or revealing their private data inputs. Secure MPC, as a cryptographic technique, is only implemented selectively on larger workflows. [1]. The real-world implementation of MPC faces the following challenges:

  • The integration of multi-party computation with data processing systems and analytics workflows is often poor.
  • Enable to run analytics in a multi-party computation framework, expert knowledge must be significant.
  • Due to MPC frameworks incapability to support data-parallel processing outside the MPC, the scaling of frameworks to large data sets is often poor.

Real-world examples of MPCs application include; Boston wage gap, Google advertising conversion, MPC for cryptographic key protection, Government Collaboration and Privacy-preserving analytics. [2].

In this work, the adversaries, challenges and applications of secure MPCs are addressed. This will help establish the viability of implementing MPCs for cases involving large data sets.

The main goal of secure MPC is to allow different data owners who might not trust each other to unite in the computation of a function which depends on their private data inputs. All of the participants involved in MPC computations are data input owners. [3].

2   Mpc Properties And Protocols

Evans, Kolesnikov, and Rosulek (2018), used a classical example of an MPC problem involving large data sets. Here, two millionaires wanted to know who was richer without knowing the net worth of each other. However, it is mandatory to first understand the basic roles of MPC systems:

  • Input Parties (IP) sending data to the private computation
  • Result Parties (RP) who get results from the private computation
  • Computing Parties (CP) who do the joint private computation.

The most common protocol in the MPC system, for each person or organization involved in these roles, is that there is no single point of trust. This means, none of the computing parties can gain access to the encrypted source data. There are several properties that the MPC protocols have, so as to enhance the security, robustness and efficiency of the MPC systems. The most important protocols are as follows:

  • n, which is the number of CPs that are to be involved in the system
  • f, the maximum number of CPs that are allowed to run the intended protocol or regulate the MPC system. f + 1 can be addressed as a violation of the system.
  • Passive security which guarantees source data privacy such that the CPs involved in the MPC system execute reliable protocols.
  • Abort active security, which ensures that the corrupt CP runs the purported protocol or else, the protocol is aborted.
  • Fault tolerance Active Security; ensures that the system continues to operate even when a CP has ceased to operate correctly.

MPC does not depend on the trustworthiness of institutions or individuals. It can, also, be used to create a neutral MPC from combining many other trusted entities. Another solution can be in combining trusted entities with those having opposing interests so as to create a more reliable MPC. Implementing the protocols discussed earlier on relevant applications will guarantee a more optimal result. [3]

3   Adversaries And Mitigation Strategies

Security of the MPC system is of vital consideration and we regard it as being secure against threat complexities if an ideal adversary simulation of a real adversary attacking the MPC protocols in an ideal world can be made. [4]. Since secure MPC’s deal with private big data computations, there are many adversaries who can end up being attracted to invading and accessing the encrypted inputs. These adversaries can be categorized as semi-honest, malicious or covert.

  • Semi-honest adversaries are called Honest-but-curious. These are honest in terms of ensuring proper execution of the MPC system to acquire an accurate evaluation, but they are also curious to revealing the private input of the participating entities.
  • Malicious adversaries violate the agreed protocol so that they manipulate the computation output or learn the private data provided by the participants.
  • Covert adversaries, on the other hand, are more like malicious adversaries except that their intention is to cheat and eventually, never caught or apprehended.

These classes of adversaries can be static or adaptive. Static adversaries will attack the computation system before it is executed. Adaptive adversaries are liable to invade parties at any stage during the computation process; this renders them more difficult to defend. The success of these adversaries is determined by many other factors such as the number of participants that they can corrupt. [5]. Adversaries are the most security concern when it comes to MPCs. Sometimes the corruption of one party involved in the computation can compromise the efficiency and security of the whole computation. If all components involved in the system are corrupted or invaded, the complexity can be difficult to solve. [6]. Adaptive adversaries maybe come through as human fraud, weaknesses in the operating system or viruses. A good way to counteract such security breaches is by recovering and reusing components of the system that may have been corrupted. Transient break-ins can be overcome using the following approach:

  • Tasks and responsibilities are branched and channeled through different components. This will allow the overall security to remain intact in case one of the parties has been invaded.
  • A mechanism for automatic recovery can be designed and used for one component with the help of other components such that automatic recovery occurs once that component is no longer corrupt.
  • Install an automatic periodic recovery system mechanism for all the components involved in the computation. [7].

Other mitigation strategies to avoid complexities on MPC systems are Statistical security and computational security. [8].

The application of secure MPC systems is not sufficient for all applications as there are some limitations. A common example of these limitations is deep learning. Other limitations are due to the availability of expertise or software frameworks. Machine learning libraries for MPCs are not readily available and there is a limited availability of equivalent systems such as R or Scikit-learn. Implementation of MPCs on big data requires much expertise ad this becomes one of its limitations. [9].

4   Important Properties To Consider

Merging MPC systems for big data processing is a major concern as there are no efficient protocols or advanced communication developments. In processing big data, there are certain paramount properties to consider and these include:

  • Exploiting random access; in order to enable a secure computation, large data sets are branched by converting a program into a circuit. This makes the option less feasible.
  • Exploiting Parallelism; this helps in solving big data problems effectively. An example is Parallel RAM which allows CPUs communicate with each other whilst accessing the same shared external memory.
  • Exploiting Plurality of Users; since large number of parties can run in parallel RAMs must be secured by balancing the load across all nodes.
  • Communication Locality; to avoid high costs in establishing communication channels involving large number of parties, the locality of communication can be minimized so that communications are transmitted simultaneously during the progression of the protocol. [10]

5   Conclusion

In today’s real-world development of business analytics, computations involving big data have attracted many adversaries whose intention is to access the private data of business entities. This has led to the development of MPCs which are implemented to compute a satisfying result without the participants being privy to the parallel private data. However, for the MPCs to be more secure, we can deduct that there is need for proper implementation of regulations or protocols by operators in the MPC system. The types of adversaries must be known so as to develop mitigation strategies that can help secure private input data. In this paper, it shows that adherence to certain protocols require awareness and expertise from the participants. Satisfying the latter means the implementation of MPCs with big data can be feasible and more secure.

Acknowledgment

The authors wish to thank A, B, C. This work was supported in part by a grant from XYZ.

References

[1] N. Volgushev, M. Schwarzkopf, A. Lapets, M. Varia and A. Bestavros, “DEMO: Integrating MPC in Big Data Workflows,” CCS’16, p. http://dx.doi.org/10.1145/2976749.2989034, 24-28 Oct 2016.
[2] Y. Lindell, “Secure Multiparty Computation (MPC),” 2020. [Online]. Available: http://www.eprint.iacr.org/2020/300.pdf.
[3] D. Evans, V. Kolesnikov and M. Rosulek, “A Pragmatic Introduction to Secure Multi-Party Computation,” Foundations and Trends® in Privacy and Security, pp. 2-3, 2018.
[4] C. Orlandi, “Is Multiparty Computation Any Good In Practice?,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 2011.
[5] J. I. Choi and K. R. B. Butler, “Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities,” Security and Communication Networks, p. 28, 2019.
[6] Y. Ishai, M. Mittal and R. Ostrovsky, “On the Message Complexity of Secure Multiparty Computation,” in IACR International Workshop on Public Key Cryptography, 2018.
[7] R. Canetti, “Studies in Secure Multiparty Computation and Applications,” Scientific Council of The Weizmann Institute of Science, 1996.
[8] A. Aly, “Network Flow Problems with Secure Multiparty Computation,” PhD Organization: Universit´e catholique de Louvain, p. 151, n.d.
[9] P. Koster, “Secure MultiParty Computation (MPC) for Big Data Analytics: technology readiness from an enterprise perspective,” November 2019. [Online]. Available: https://www.solar-project.eu.
[10] E. Boyle, K.-M. Chung and R. Pass, “Large-Scale Secure Computation: Multi-party Computation for (Parallel) RAM Programs,” Advances in Cryptology, pp. 742-762, 2015.