May  2021, 1(2): 75-91. doi: 10.3934/steme.2021006

Embedding opportunities for participation and feedback in large mathematics lectures via audience response systems

1. 

School of Mathematics and Statistics, The University of New South Wales, Sydney NSW 2052, Australia

2. 

Institute for Teaching and Learning Innovation (ITaLI), University of Queensland, Brisbane Qld 4072, Australia

* Correspondence: cct@unsw.edu.au; Tel: +61-2-93856792

Received  March 2021 Revised  April 2021 Published  May 2021

The purpose of this work is to interpret the experiences of students when audience response systems (ARS) were implemented as a strategy for teaching large mathematics lecture groups at university. Our paper makes several contributions to the literature. Firstly, we furnish a basic model of how ARS can form a teaching and learning strategy. Secondly, we examine the impact of this strategy on student attitudes of their experiences, focusing on the ability of ARS to: assess understanding; identify strengths and weaknesses; furnish feedback; support learning; and to encourage participation. Our findings support the position that there is a place for ARS as part of a strategy for teaching and learning mathematics in large groups.

Citation: Christopher C. Tisdell. Embedding opportunities for participation and feedback in large mathematics lectures via audience response systems. STEM Education, 2021, 1 (2) : 75-91. doi: 10.3934/steme.2021006
References:
[1]

I.E. Allen and C.A. Seaman, Likert scales and data analyses, Quality Progress, 40 (2007), p. 64-65.   Google Scholar

[2]

Archer, M.S., Bhaskar, R., Collier, A., Lawson, T., Norrie, A. Critical Realism: Essential Readings. 2009, London, UK: Routledge. Google Scholar

[3]

Bagley, S.F., Improving student success in calculus: a comparison of four college calculus classes[dissertation]. 2014, San Diego State University: San Diego, USA. Google Scholar

[4]

Baker, J.W., The 'classroom flip': using web course management tools to become the guide by the side, in Selected Papers from the 11th International Conference on College Teaching and Learning. 2001, Florida Community College at Jacksonville: Jacksonville (FL), p. 9–17. Google Scholar

[5]

Banks, D.A., Audience Response Systems in Higher Education: Applications and Cases. 2006, Hershey, PA, USA: Information Science Publishing. Google Scholar

[6]

Berends, M., Survey methods in educational research, in Handbook of Complementary Methods in Education Research, J.L. Green, G. Camilli, P.B. Elmore, Ed. 2006, Lawrence Erlbaum Associates. p. 623-640. Retrieved from http://psycnet.apa.org/record/2006-05382-038 Google Scholar

[7]

Bligh, D.A., What's the Use of Lectures? 1972, Harmondsworth, UK: Penguin Books. Google Scholar

[8]

Bonwell, C.C., Eison, J.A., Active Learning: Creating Excitement in the Classroom. 2005, San Francisco, USA: Jossey-Bass. Google Scholar

[9]

Box, G.E., Robustness in the Strategy of Scientific Model Building. 1979, Ft. Belvoir: Defense Technical Information Center. Retrieved from http://www.dtic.mil/docs/citations/ADA070213 Google Scholar

[10]

S. ChenS.J. Yang and C. Hsiao, Exploring student perceptions, learning outcome and gender differences in a flipped mathematics course, British Journal of Educational Technology, 47 (2016), p. 1096-1112.  doi: 10.1111/bjet.12278.  Google Scholar

[11]

Codecogs. Retrieved from https://www.codecogs.com/latex/eqneditor.php?latex=D Google Scholar

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Coe, R., Waring, M., Hedges, L.V., Arthur, J., Research Methods and Methodologies in Education. 2017, Los Angeles, CA: SAGE. Google Scholar

[13]

Cohen, J., Statistical Power Analysis for the Behavioral Sciences. 1988, Abingdon-on-Thames, UK: Routledge. Google Scholar

[14]

J. Cohen, Things I have learned (so far), Am Psychol, (1990), p. 1304-1312.   Google Scholar

[15]

Cohen, L., Manion, L., Morrison, K., Research Methods in Education. 2018, London: Routledge. Google Scholar

[16]

Creswell, J.W., Qualitative Inquiry and Research Design: Choosing among Five Approaches. 2007, Thousand Oaks, CA: Sage. Google Scholar

[17]

Cronhjort, M., Filipsson, L., Weurlander, M., Improved engagement and learning in flipped-classroom calculus. Teaching Mathematics and its Applications: An International Journal of the IMA, 2018. 37(3): p. 113–121. doi: 10.1093/teamat/hrx007. Google Scholar

[18]

Day A.L., Case study research, in Research Methods & Methodologies in Education, 2nd ed. R. Coe, M. Waring, L.V. Hedges, J. Arthur, Ed. 2017, Los Angeles, CA: SAGE. p. 114-121. Google Scholar

[19]

Duncan, D., Clickers in the Classroom: How to Enhance Science Teaching Using Classroom Response Systems. 2005, San Francisco, CA: Pearson Education. Google Scholar

[20]

P.K. DunnA. RichardsonC. McDonald and F. Oprescu, Instructor perceptions of using a mobile-phone-based free classroom response system in first-year statistics undergraduate courses, International Journal of Mathematical Education in Science and Technology, 43 (2012), p. 1041-1056.  doi: 10.1080/0020739x.2012.678896.  Google Scholar

[21]

P.K. DunnA. RichardsonF. Oprescu and C. McDonald, Mobile-phone-based classroom response systems: Students' perceptions of engagement and learning in a large undergraduate course, International Journal of Mathematical Education in Science and Technology, 44 (2013), p. 1160-1174.  doi: 10.1080/0020739x.2012.756548.  Google Scholar

[22]

Google, Create forms. 2017. Retrieved from https://www.google.com.au/forms/about/ Google Scholar

[23]

Higher Education Research & Development Society of Australasia, HERDSA Fellowship Scheme Handbook. 2014, Milperra, NSW: HERSDSA. Retrieved from https://www.herdsa.org.au/sites/default/files/Fellowship\%20Handbook_6_5_2014.pdf Google Scholar

[24]

B.M. Johnston, Implementing a flipped classroom approach in a university numerical methods mathematics course, International Journal of Mathematical Education in Science and Technology, 48 (2017), p. 485-498.  doi: 10.1080/0020739x.2016.1259516.  Google Scholar

[25]

V. JungicH. KaurJ. Mulholland and C. Xin, On flipping the classroom in large first year calculus courses, International Journal of Mathematical Education in Science and Technology, 46 (2015), p. 508-520.  doi: 10.1080/0020739x.2014.990529.  Google Scholar

[26]

S.O. King and C.L. Robinson, 'Pretty lights' and maths! Increasing student engagement and enhancing learning through the use of electronic voting systems, Computers & Education, 53 (2009), p. 189-199.  doi: 10.1016/j.compedu.2009.01.012.  Google Scholar

[27]

Kline, R.B., Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research. 2004, Washington, DC: American Psychological Association, p. 95. Google Scholar

[28]

K. Larkin and N. Calder, Mathematics education and mobile technologies, Math Ed Res J, 28 (2016), p. 1-7.  doi: 10.1007/s13394-015-0167-6.  Google Scholar

[29]

Lomen, D.O., Robinson, M.K., Using ConcepTests in single and multivariable calculus, in Electronic Proceedings of the Sixteenth Annual International Conference on Technology in Collegiate Mathematics. 2005. Retrieved October 17, 2017 from http://archives.math.utk.edu/ICTCM/i/16/S107.html Google Scholar

[30]

B. LoveA. HodgeN. Grandgenett and A.W. Swift, Student learning and perceptions in a flipped linear algebra course, International Journal of Mathematical Education in Science and Technology, 45 (2014), p. 317-324.  doi: 10.1080/0020739x.2013.822582.  Google Scholar

[31]

W. Maciejewski, Flipping the calculus classroom: an evaluative study, Teaching Mathematics and its Applications, 35 (2016), p. 187-201.  doi: 10.1093/teamat/hrv019.  Google Scholar

[32]

Markie, P., Rationalism vs. Empiricism. 2017. Retrieved from https://plato.stanford.edu/entries/rationalism-empiricism/ Google Scholar

[33]

J. MurphyJ. Chang and K. Suaray, Student performance and attitudes in a collaborative and flipped linear algebra course, International Journal of Mathematical Education in Science and Technology, 47 (2015), p. 653-673.  doi: 10.1080/0020739x.2015.1102979.  Google Scholar

[34]

E. Naccarato and G. Karakok, Expectations and implementations of the flipped classroom model in undergraduate mathematics courses, International Journal of Mathematical Education in Science and Technology, 46 (2015), p. 968-978.  doi: 10.1080/0020739x.2015.1071440.  Google Scholar

[35]

von Neumann, J., The mathematician, in Works of the Mind, R.B., Haywood Ed. 1947, Chicago: University of Chicago Press. p. 180-196. Google Scholar

[36]

Novak, J., Kensington-Miller, B., Evans, T., Flip or flop? Students' perspectives of a flipped lecture in mathematics. International Journal of Mathematical Education in Science and Technology, 2017. 48(5): p. 647-658. doi: 10.1080/0020739x.2016.1267810. Google Scholar

[37]

Center for Educational Research and Innovation, Giving Knowledge for Free: The Emergence of Open Educational Resources. Retrieved from http://www.oecd.org/edu/ceri/38654317.pdf Google Scholar

[38]

J. Petrillo, On flipping first-semester calculus: a case study, International Journal of Mathematical Education in Science and Technology, 47 (2016), p. 573-582.  doi: 10.1080/0020739x.2015.1106014.  Google Scholar

[39]

Robson, L., Guide to Evaluating the Effectiveness of Strategies for Preventing Work Injuries: How to Show Whether a Safety Intervention Really Works. 2001, Cincinnati, OH: DHHS. Google Scholar

[40]

R. Salzer, Smartphones as audience response systems for lectures and seminars, Analytical and Bioanalytical Chemistry, 410 (2018), p. 1609-1613.  doi: 10.1007/s00216-017-0794-8.  Google Scholar

[41]

S. Sawilowsky, New effect size rules of thumb, Journal of Modern Applied Statistical Methods, 8 (2009), p. 467-474.  doi: 10.22237/jmasm/1257035100.  Google Scholar

[42]

Shadish, W.R., Cook, T.D., Campbell, D.T., Experimental and Quasi-experimental Designs for Generalized Causal Inference. 2001, Belmont, CA: Wadsworth Cengage Learning. Google Scholar

[43]

G. M. Sullivan and Jr. A.R. Artino, Analyzing and interpreting data from Likert-type scales, Journal of Graduate Medical Education, 5 (2013), p. 541-542.   Google Scholar

[44]

C.C. Tisdell, Critical perspectives of pedagogical approaches to reversing the order of integration in double integrals, International Journal of Mathematical Education in Science and Technology, 48 (2017), p. 1285-1292.  doi: 10.1080/0020739X.2017.1329559.  Google Scholar

[45]

C.C. Tisdell, Pedagogical alternatives for triple integrals: moving towards more inclusive and personalized learning, International Journal of Mathematical Education in Science and Technology, 49 (2018), p. 792-801.  doi: 10.1080/0020739X.2017.1408150.  Google Scholar

[46]

C.C. Tisdell, On Picard's iteration method to solve differential equations and a pedagogical space for otherness, International Journal of Mathematical Education in Science and Technology, 50 (2019), p. 788-799.  doi: 10.1080/0020739X.2018.1507051.  Google Scholar

[47]

C.C. Tisdell, Schoenfeld's problem-solving models viewed through the lens of exemplification, For the Learning of Mathematics, 39 (2019), p. 24-26.   Google Scholar

[48]

Trochim, W.M.K., The Research Methods Knowledge Base. 2006. Retrieved from https://www.socialresearchmethods.net/kb/positvsm.php Google Scholar

[49]

University of Edinburgh. What is digital education? 2019. Retrieved from https://www.ed.ac.uk/institute-academic-development/learning-teaching/staff/digital-ed/what-is-digital-education Google Scholar

[50]

Wang, V.C., Handbook of Research on e-learning Applications for Career and Technical Education: Technologies for Vocational Training. 2009, Hershey, PA: IGI Global. Google Scholar

[51]

Wasserman, N., Norris, S., Carr, T., Comparing a "flipped" instructional model in an undergraduate Calculus Ⅲ course, in Proceedings of the 16th Annual Conference on Research in Undergraduate Mathematics Education; S. Brown, G. Karakok, K.H. Roh, M. Oehrtman Ed..2013, Denver, CO. Google Scholar

[52]

Yin, R.K., Case Study Research: Design and Methods. 4th ed. 2009, Thousand Oaks, CA: Sage. Google Scholar

show all references

References:
[1]

I.E. Allen and C.A. Seaman, Likert scales and data analyses, Quality Progress, 40 (2007), p. 64-65.   Google Scholar

[2]

Archer, M.S., Bhaskar, R., Collier, A., Lawson, T., Norrie, A. Critical Realism: Essential Readings. 2009, London, UK: Routledge. Google Scholar

[3]

Bagley, S.F., Improving student success in calculus: a comparison of four college calculus classes[dissertation]. 2014, San Diego State University: San Diego, USA. Google Scholar

[4]

Baker, J.W., The 'classroom flip': using web course management tools to become the guide by the side, in Selected Papers from the 11th International Conference on College Teaching and Learning. 2001, Florida Community College at Jacksonville: Jacksonville (FL), p. 9–17. Google Scholar

[5]

Banks, D.A., Audience Response Systems in Higher Education: Applications and Cases. 2006, Hershey, PA, USA: Information Science Publishing. Google Scholar

[6]

Berends, M., Survey methods in educational research, in Handbook of Complementary Methods in Education Research, J.L. Green, G. Camilli, P.B. Elmore, Ed. 2006, Lawrence Erlbaum Associates. p. 623-640. Retrieved from http://psycnet.apa.org/record/2006-05382-038 Google Scholar

[7]

Bligh, D.A., What's the Use of Lectures? 1972, Harmondsworth, UK: Penguin Books. Google Scholar

[8]

Bonwell, C.C., Eison, J.A., Active Learning: Creating Excitement in the Classroom. 2005, San Francisco, USA: Jossey-Bass. Google Scholar

[9]

Box, G.E., Robustness in the Strategy of Scientific Model Building. 1979, Ft. Belvoir: Defense Technical Information Center. Retrieved from http://www.dtic.mil/docs/citations/ADA070213 Google Scholar

[10]

S. ChenS.J. Yang and C. Hsiao, Exploring student perceptions, learning outcome and gender differences in a flipped mathematics course, British Journal of Educational Technology, 47 (2016), p. 1096-1112.  doi: 10.1111/bjet.12278.  Google Scholar

[11]

Codecogs. Retrieved from https://www.codecogs.com/latex/eqneditor.php?latex=D Google Scholar

[12]

Coe, R., Waring, M., Hedges, L.V., Arthur, J., Research Methods and Methodologies in Education. 2017, Los Angeles, CA: SAGE. Google Scholar

[13]

Cohen, J., Statistical Power Analysis for the Behavioral Sciences. 1988, Abingdon-on-Thames, UK: Routledge. Google Scholar

[14]

J. Cohen, Things I have learned (so far), Am Psychol, (1990), p. 1304-1312.   Google Scholar

[15]

Cohen, L., Manion, L., Morrison, K., Research Methods in Education. 2018, London: Routledge. Google Scholar

[16]

Creswell, J.W., Qualitative Inquiry and Research Design: Choosing among Five Approaches. 2007, Thousand Oaks, CA: Sage. Google Scholar

[17]

Cronhjort, M., Filipsson, L., Weurlander, M., Improved engagement and learning in flipped-classroom calculus. Teaching Mathematics and its Applications: An International Journal of the IMA, 2018. 37(3): p. 113–121. doi: 10.1093/teamat/hrx007. Google Scholar

[18]

Day A.L., Case study research, in Research Methods & Methodologies in Education, 2nd ed. R. Coe, M. Waring, L.V. Hedges, J. Arthur, Ed. 2017, Los Angeles, CA: SAGE. p. 114-121. Google Scholar

[19]

Duncan, D., Clickers in the Classroom: How to Enhance Science Teaching Using Classroom Response Systems. 2005, San Francisco, CA: Pearson Education. Google Scholar

[20]

P.K. DunnA. RichardsonC. McDonald and F. Oprescu, Instructor perceptions of using a mobile-phone-based free classroom response system in first-year statistics undergraduate courses, International Journal of Mathematical Education in Science and Technology, 43 (2012), p. 1041-1056.  doi: 10.1080/0020739x.2012.678896.  Google Scholar

[21]

P.K. DunnA. RichardsonF. Oprescu and C. McDonald, Mobile-phone-based classroom response systems: Students' perceptions of engagement and learning in a large undergraduate course, International Journal of Mathematical Education in Science and Technology, 44 (2013), p. 1160-1174.  doi: 10.1080/0020739x.2012.756548.  Google Scholar

[22]

Google, Create forms. 2017. Retrieved from https://www.google.com.au/forms/about/ Google Scholar

[23]

Higher Education Research & Development Society of Australasia, HERDSA Fellowship Scheme Handbook. 2014, Milperra, NSW: HERSDSA. Retrieved from https://www.herdsa.org.au/sites/default/files/Fellowship\%20Handbook_6_5_2014.pdf Google Scholar

[24]

B.M. Johnston, Implementing a flipped classroom approach in a university numerical methods mathematics course, International Journal of Mathematical Education in Science and Technology, 48 (2017), p. 485-498.  doi: 10.1080/0020739x.2016.1259516.  Google Scholar

[25]

V. JungicH. KaurJ. Mulholland and C. Xin, On flipping the classroom in large first year calculus courses, International Journal of Mathematical Education in Science and Technology, 46 (2015), p. 508-520.  doi: 10.1080/0020739x.2014.990529.  Google Scholar

[26]

S.O. King and C.L. Robinson, 'Pretty lights' and maths! Increasing student engagement and enhancing learning through the use of electronic voting systems, Computers & Education, 53 (2009), p. 189-199.  doi: 10.1016/j.compedu.2009.01.012.  Google Scholar

[27]

Kline, R.B., Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research. 2004, Washington, DC: American Psychological Association, p. 95. Google Scholar

[28]

K. Larkin and N. Calder, Mathematics education and mobile technologies, Math Ed Res J, 28 (2016), p. 1-7.  doi: 10.1007/s13394-015-0167-6.  Google Scholar

[29]

Lomen, D.O., Robinson, M.K., Using ConcepTests in single and multivariable calculus, in Electronic Proceedings of the Sixteenth Annual International Conference on Technology in Collegiate Mathematics. 2005. Retrieved October 17, 2017 from http://archives.math.utk.edu/ICTCM/i/16/S107.html Google Scholar

[30]

B. LoveA. HodgeN. Grandgenett and A.W. Swift, Student learning and perceptions in a flipped linear algebra course, International Journal of Mathematical Education in Science and Technology, 45 (2014), p. 317-324.  doi: 10.1080/0020739x.2013.822582.  Google Scholar

[31]

W. Maciejewski, Flipping the calculus classroom: an evaluative study, Teaching Mathematics and its Applications, 35 (2016), p. 187-201.  doi: 10.1093/teamat/hrv019.  Google Scholar

[32]

Markie, P., Rationalism vs. Empiricism. 2017. Retrieved from https://plato.stanford.edu/entries/rationalism-empiricism/ Google Scholar

[33]

J. MurphyJ. Chang and K. Suaray, Student performance and attitudes in a collaborative and flipped linear algebra course, International Journal of Mathematical Education in Science and Technology, 47 (2015), p. 653-673.  doi: 10.1080/0020739x.2015.1102979.  Google Scholar

[34]

E. Naccarato and G. Karakok, Expectations and implementations of the flipped classroom model in undergraduate mathematics courses, International Journal of Mathematical Education in Science and Technology, 46 (2015), p. 968-978.  doi: 10.1080/0020739x.2015.1071440.  Google Scholar

[35]

von Neumann, J., The mathematician, in Works of the Mind, R.B., Haywood Ed. 1947, Chicago: University of Chicago Press. p. 180-196. Google Scholar

[36]

Novak, J., Kensington-Miller, B., Evans, T., Flip or flop? Students' perspectives of a flipped lecture in mathematics. International Journal of Mathematical Education in Science and Technology, 2017. 48(5): p. 647-658. doi: 10.1080/0020739x.2016.1267810. Google Scholar

[37]

Center for Educational Research and Innovation, Giving Knowledge for Free: The Emergence of Open Educational Resources. Retrieved from http://www.oecd.org/edu/ceri/38654317.pdf Google Scholar

[38]

J. Petrillo, On flipping first-semester calculus: a case study, International Journal of Mathematical Education in Science and Technology, 47 (2016), p. 573-582.  doi: 10.1080/0020739x.2015.1106014.  Google Scholar

[39]

Robson, L., Guide to Evaluating the Effectiveness of Strategies for Preventing Work Injuries: How to Show Whether a Safety Intervention Really Works. 2001, Cincinnati, OH: DHHS. Google Scholar

[40]

R. Salzer, Smartphones as audience response systems for lectures and seminars, Analytical and Bioanalytical Chemistry, 410 (2018), p. 1609-1613.  doi: 10.1007/s00216-017-0794-8.  Google Scholar

[41]

S. Sawilowsky, New effect size rules of thumb, Journal of Modern Applied Statistical Methods, 8 (2009), p. 467-474.  doi: 10.22237/jmasm/1257035100.  Google Scholar

[42]

Shadish, W.R., Cook, T.D., Campbell, D.T., Experimental and Quasi-experimental Designs for Generalized Causal Inference. 2001, Belmont, CA: Wadsworth Cengage Learning. Google Scholar

[43]

G. M. Sullivan and Jr. A.R. Artino, Analyzing and interpreting data from Likert-type scales, Journal of Graduate Medical Education, 5 (2013), p. 541-542.   Google Scholar

[44]

C.C. Tisdell, Critical perspectives of pedagogical approaches to reversing the order of integration in double integrals, International Journal of Mathematical Education in Science and Technology, 48 (2017), p. 1285-1292.  doi: 10.1080/0020739X.2017.1329559.  Google Scholar

[45]

C.C. Tisdell, Pedagogical alternatives for triple integrals: moving towards more inclusive and personalized learning, International Journal of Mathematical Education in Science and Technology, 49 (2018), p. 792-801.  doi: 10.1080/0020739X.2017.1408150.  Google Scholar

[46]

C.C. Tisdell, On Picard's iteration method to solve differential equations and a pedagogical space for otherness, International Journal of Mathematical Education in Science and Technology, 50 (2019), p. 788-799.  doi: 10.1080/0020739X.2018.1507051.  Google Scholar

[47]

C.C. Tisdell, Schoenfeld's problem-solving models viewed through the lens of exemplification, For the Learning of Mathematics, 39 (2019), p. 24-26.   Google Scholar

[48]

Trochim, W.M.K., The Research Methods Knowledge Base. 2006. Retrieved from https://www.socialresearchmethods.net/kb/positvsm.php Google Scholar

[49]

University of Edinburgh. What is digital education? 2019. Retrieved from https://www.ed.ac.uk/institute-academic-development/learning-teaching/staff/digital-ed/what-is-digital-education Google Scholar

[50]

Wang, V.C., Handbook of Research on e-learning Applications for Career and Technical Education: Technologies for Vocational Training. 2009, Hershey, PA: IGI Global. Google Scholar

[51]

Wasserman, N., Norris, S., Carr, T., Comparing a "flipped" instructional model in an undergraduate Calculus Ⅲ course, in Proceedings of the 16th Annual Conference on Research in Undergraduate Mathematics Education; S. Brown, G. Karakok, K.H. Roh, M. Oehrtman Ed..2013, Denver, CO. Google Scholar

[52]

Yin, R.K., Case Study Research: Design and Methods. 4th ed. 2009, Thousand Oaks, CA: Sage. Google Scholar

Figure 1.  Sample Question from Quiz: Q1ii)
Figure 2.  Sample Question from Quiz: Q2i)
Figure 3.  Sample Question from Quiz: Q2ii)
Figure 4.  Class Responses to Question 1ii) of Quiz
Figure 5.  Class Responses to Question 2i) of Quiz
Figure 6.  Class Responses to Question 2ii) of Quiz
Table 1.  Groups of Interest
Group Details
Target: Undergraduate students in large mathematics classes
Sample: Students in Lecture Group 1 of MATH1131 during the algebra lectures where the intervention took place
Comparison: Students in Lecture Group 1 of MATH1131 during the calculus lectures where no intervention took place
Group Details
Target: Undergraduate students in large mathematics classes
Sample: Students in Lecture Group 1 of MATH1131 during the algebra lectures where the intervention took place
Comparison: Students in Lecture Group 1 of MATH1131 during the calculus lectures where no intervention took place
Table 2.  Themes and Relevant Components of Audience Response Systems
Theme Relevant Components
ARS: Lecture (re)design and (re)deliveryEmbedding of discussion, assessment and feedback
Digital Education: Use of mobile devices (laptops, phones, tabletsCreation of YouTube videos
Open Educational Resources: Use of Google Forms
Theme Relevant Components
ARS: Lecture (re)design and (re)deliveryEmbedding of discussion, assessment and feedback
Digital Education: Use of mobile devices (laptops, phones, tabletsCreation of YouTube videos
Open Educational Resources: Use of Google Forms
Table 3.  Summary of Intervention: Framework for Delivery
Activity Technology
Deliver material at start of lecture None necessarily required
Assess material via short, formative quiz Accessed via Google Forms / responses via m-devices on wifi or network
Feedback to class on the results of quiz Discuss results via graphs from Google Forms
Discussion and thoughts on how to improve (if needed) None necessarily required
Activity Technology
Deliver material at start of lecture None necessarily required
Assess material via short, formative quiz Accessed via Google Forms / responses via m-devices on wifi or network
Feedback to class on the results of quiz Discuss results via graphs from Google Forms
Discussion and thoughts on how to improve (if needed) None necessarily required
Table 4.  Evaluation Overview
Evaluation Approach Timing/Sample/Analysis Evaluation Focus
Attitude Data 1: (Sample Group) Post intervention. Attitudinal data collected from bespoke survey of 348 students within component of intervention (algebra lectures). Six-point Likert scale employed; mean values calculated, including 95% confidence intervals. Comments collected and coded. Impact on students' attitudes towards their learning experience.
Attitude Data 2: (Sample Group) 3 weeks after Survey 1. Attitudinal data collected from survey of ~180 students within component of intervention (algebra lectures). This is a subset of the 348 students from previous survey. Six-point Likert scale employed; mean values calculated, including 95% confidence intervals. Comments collected and coded. Sample Group and Control Group compared via statistical tests. Impact on students' attitudes towards their learning experience.
Attitude Data 3: (Control Group) 3 weeks after Survey 1. Attitudinal data collected from survey of 102 students within component where no intervention took place (calculus lectures). This is a subset of the 348 students from previous survey. Six-point Likert scale employed; mean values calculated, including 95% confidence intervals. Sample Group and Control Group compared via statistical tests. Impact on students' attitudes towards their learning experience.
Evaluation Approach Timing/Sample/Analysis Evaluation Focus
Attitude Data 1: (Sample Group) Post intervention. Attitudinal data collected from bespoke survey of 348 students within component of intervention (algebra lectures). Six-point Likert scale employed; mean values calculated, including 95% confidence intervals. Comments collected and coded. Impact on students' attitudes towards their learning experience.
Attitude Data 2: (Sample Group) 3 weeks after Survey 1. Attitudinal data collected from survey of ~180 students within component of intervention (algebra lectures). This is a subset of the 348 students from previous survey. Six-point Likert scale employed; mean values calculated, including 95% confidence intervals. Comments collected and coded. Sample Group and Control Group compared via statistical tests. Impact on students' attitudes towards their learning experience.
Attitude Data 3: (Control Group) 3 weeks after Survey 1. Attitudinal data collected from survey of 102 students within component where no intervention took place (calculus lectures). This is a subset of the 348 students from previous survey. Six-point Likert scale employed; mean values calculated, including 95% confidence intervals. Sample Group and Control Group compared via statistical tests. Impact on students' attitudes towards their learning experience.
Table 5.  Statements in Survey 1 (A-F) and Survey 2 (G-H)
Item Statement
A The quizzes provided a valuable opportunity to test my understanding of basic ideas
B The quizzes helped to identify specific strengths and weaknesses of my understanding
C It was valuable to have immediate feedback and discussion of the results
D The quizzes encouraged and supported my learning
E Overall, I was satisfied that these quizzes were a valuable learning tool
F I would like to have these kinds of quizzes available to support my learning in future lectures
G This lecturer provided feedback to help me learn
H This lecturer encouraged student input and participation during classes
Item Statement
A The quizzes provided a valuable opportunity to test my understanding of basic ideas
B The quizzes helped to identify specific strengths and weaknesses of my understanding
C It was valuable to have immediate feedback and discussion of the results
D The quizzes encouraged and supported my learning
E Overall, I was satisfied that these quizzes were a valuable learning tool
F I would like to have these kinds of quizzes available to support my learning in future lectures
G This lecturer provided feedback to help me learn
H This lecturer encouraged student input and participation during classes
Table 6.  Responses of Sample Group to Survey 1 and 2
Statement Strongly Disagree Disagree MildlyDisagree Mildly Agree Agree StronglyAgree n
A 3 1 1 16 149 178 348
B 3 2 7 44 157 135 348
C 3 0 2 18 115 210 348
D 3 2 1 46 165 131 348
E 3 2 1 24 163 155 348
F 3 0 1 15 136 193 348
G 1 4 2 24 66 81 178
H 0 1 1 4 44 131 181
Statement Strongly Disagree Disagree MildlyDisagree Mildly Agree Agree StronglyAgree n
A 3 1 1 16 149 178 348
B 3 2 7 44 157 135 348
C 3 0 2 18 115 210 348
D 3 2 1 46 165 131 348
E 3 2 1 24 163 155 348
F 3 0 1 15 136 193 348
G 1 4 2 24 66 81 178
H 0 1 1 4 44 131 181
Table 7.  Responses of Control Group to Survey 1 and 2
Statement Strongly Disagree Disagree MildlyDisagree Mildly Agree Agree StronglyAgree n
G 2 10 17 37 26 10 102
H 5 11 22 37 19 9 103
Statement Strongly Disagree Disagree MildlyDisagree Mildly Agree Agree StronglyAgree n
G 2 10 17 37 26 10 102
H 5 11 22 37 19 9 103
Table 8.  Analysis of Responses of Sample Group to Survey 1 and 2
Statement Mean Score/ 6 ConfidenceInterval 95% % OverallAgree* StandardDeviation ofMean n
A 5.42 ±0.08 99 0.75 348
B 5.17 ±0.09 97 0.82 348
C 5.51 ±0.08 99 0.75 348
D 5.19 ±0.09 98 0.82 348
E 5.31 ±0.08 98 0.82 348
F 5.47 ±0.08 99 0.72 348
G 5.21 ±0.14 96 0.94 178
H 5.66 ±0.10 99 0.70 181
*Overall Agreement is defined as those responses of: Mildy Agree; Agree; or Strongly Agree.
Statement Mean Score/ 6 ConfidenceInterval 95% % OverallAgree* StandardDeviation ofMean n
A 5.42 ±0.08 99 0.75 348
B 5.17 ±0.09 97 0.82 348
C 5.51 ±0.08 99 0.75 348
D 5.19 ±0.09 98 0.82 348
E 5.31 ±0.08 98 0.82 348
F 5.47 ±0.08 99 0.72 348
G 5.21 ±0.14 96 0.94 178
H 5.66 ±0.10 99 0.70 181
*Overall Agreement is defined as those responses of: Mildy Agree; Agree; or Strongly Agree.
Table 9.  Analysis of Responses of Control Group to Survey 2
Statement Mean Score/ 6 ConfidenceInterval 95% % OverallAgree* StandardDeviation ofMean n
G 4.03 ±0.23 72 1.18 102
H 3.79 ±0.24 63 1.25 104
*Overall Agreement is defined as those responses of: Mildy Agree; Agree; or Strongly Agree.
Statement Mean Score/ 6 ConfidenceInterval 95% % OverallAgree* StandardDeviation ofMean n
G 4.03 ±0.23 72 1.18 102
H 3.79 ±0.24 63 1.25 104
*Overall Agreement is defined as those responses of: Mildy Agree; Agree; or Strongly Agree.
Table 10.  Themed Comments from Sample Group in Survey 1
Theme Number
Efficacy 32
Appreciation 29
Constructive Suggestions 16
Theme Number
Efficacy 32
Appreciation 29
Constructive Suggestions 16
Table 11.  Significance Tests Between Sample Group and Control Group to Survey 2
Statement Student's t-testp < 0.05? Mann-Whitney U-test p < 0.05? Effect Size (Cohen's d)
G Yes Yes 1.10
H Yes Yes 1.84
Statement Student's t-testp < 0.05? Mann-Whitney U-test p < 0.05? Effect Size (Cohen's d)
G Yes Yes 1.10
H Yes Yes 1.84
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