
Previous Article
Good practices of delivery and teaching leadership for online educators in technical disciplines: A perspective
 STEME Home
 This Issue
 Next Article
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 
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.
References:
[1] 
I.E. Allen and C.A. Seaman, Likert scales and data analyses, Quality Progress, 40 (2007), p. 6465. 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. 623640. Retrieved from http://psycnet.apa.org/record/200605382038 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: JosseyBass. 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. Chen, S.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. 10961112. 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, AbingdononThames, UK: Routledge. Google Scholar 
[14] 
J. Cohen, Things I have learned (so far), Am Psychol, (1990), p. 13041312. 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 flippedclassroom 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. 114121. 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. Dunn, A. Richardson, C. McDonald and F. Oprescu, Instructor perceptions of using a mobilephonebased free classroom response system in firstyear statistics undergraduate courses, International Journal of Mathematical Education in Science and Technology, 43 (2012), p. 10411056. doi: 10.1080/0020739x.2012.678896. Google Scholar 
[21] 
P.K. Dunn, A. Richardson, F. Oprescu and C. McDonald, Mobilephonebased 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. 11601174. 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. 485498. doi: 10.1080/0020739x.2016.1259516. Google Scholar 
[25] 
V. Jungic, H. Kaur, J. 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. 508520. 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. 189199. 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. 17. doi: 10.1007/s1339401501676. 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. Love, A. Hodge, N. 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. 317324. 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. 187201. doi: 10.1093/teamat/hrv019. Google Scholar 
[32] 
Markie, P., Rationalism vs. Empiricism. 2017. Retrieved from https://plato.stanford.edu/entries/rationalismempiricism/ Google Scholar 
[33] 
J. Murphy, J. 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. 653673. 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. 968978. 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. 180196. Google Scholar 
[36] 
Novak, J., KensingtonMiller, 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. 647658. 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 firstsemester calculus: a case study, International Journal of Mathematical Education in Science and Technology, 47 (2016), p. 573582. 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. 16091613. doi: 10.1007/s0021601707948. Google Scholar 
[41] 
S. Sawilowsky, New effect size rules of thumb, Journal of Modern Applied Statistical Methods, 8 (2009), p. 467474. doi: 10.22237/jmasm/1257035100. Google Scholar 
[42] 
Shadish, W.R., Cook, T.D., Campbell, D.T., Experimental and Quasiexperimental 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 Likerttype scales, Journal of Graduate Medical Education, 5 (2013), p. 541542. 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. 12851292. 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. 792801. 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. 788799. doi: 10.1080/0020739X.2018.1507051. Google Scholar 
[47] 
C.C. Tisdell, Schoenfeld's problemsolving models viewed through the lens of exemplification, For the Learning of Mathematics, 39 (2019), p. 2426. 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/instituteacademicdevelopment/learningteaching/staff/digitaled/whatisdigitaleducation Google Scholar 
[50] 
Wang, V.C., Handbook of Research on elearning 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. 6465. 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. 623640. Retrieved from http://psycnet.apa.org/record/200605382038 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: JosseyBass. 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. Chen, S.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. 10961112. 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, AbingdononThames, UK: Routledge. Google Scholar 
[14] 
J. Cohen, Things I have learned (so far), Am Psychol, (1990), p. 13041312. 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 flippedclassroom 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. 114121. 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. Dunn, A. Richardson, C. McDonald and F. Oprescu, Instructor perceptions of using a mobilephonebased free classroom response system in firstyear statistics undergraduate courses, International Journal of Mathematical Education in Science and Technology, 43 (2012), p. 10411056. doi: 10.1080/0020739x.2012.678896. Google Scholar 
[21] 
P.K. Dunn, A. Richardson, F. Oprescu and C. McDonald, Mobilephonebased 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. 11601174. 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. 485498. doi: 10.1080/0020739x.2016.1259516. Google Scholar 
[25] 
V. Jungic, H. Kaur, J. 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. 508520. 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. 189199. 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. 17. doi: 10.1007/s1339401501676. 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. Love, A. Hodge, N. 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. 317324. 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. 187201. doi: 10.1093/teamat/hrv019. Google Scholar 
[32] 
Markie, P., Rationalism vs. Empiricism. 2017. Retrieved from https://plato.stanford.edu/entries/rationalismempiricism/ Google Scholar 
[33] 
J. Murphy, J. 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. 653673. 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. 968978. 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. 180196. Google Scholar 
[36] 
Novak, J., KensingtonMiller, 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. 647658. 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 firstsemester calculus: a case study, International Journal of Mathematical Education in Science and Technology, 47 (2016), p. 573582. 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. 16091613. doi: 10.1007/s0021601707948. Google Scholar 
[41] 
S. Sawilowsky, New effect size rules of thumb, Journal of Modern Applied Statistical Methods, 8 (2009), p. 467474. doi: 10.22237/jmasm/1257035100. Google Scholar 
[42] 
Shadish, W.R., Cook, T.D., Campbell, D.T., Experimental and Quasiexperimental 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 Likerttype scales, Journal of Graduate Medical Education, 5 (2013), p. 541542. 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. 12851292. 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. 792801. 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. 788799. doi: 10.1080/0020739X.2018.1507051. Google Scholar 
[47] 
C.C. Tisdell, Schoenfeld's problemsolving models viewed through the lens of exemplification, For the Learning of Mathematics, 39 (2019), p. 2426. 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/instituteacademicdevelopment/learningteaching/staff/digitaled/whatisdigitaleducation Google Scholar 
[50] 
Wang, V.C., Handbook of Research on elearning 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 
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 
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 
Activity  Technology 
Deliver material at start of lecture  None necessarily required 
Assess material via short, formative quiz  Accessed via Google Forms / responses via mdevices 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 mdevices 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 
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). Sixpoint 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. Sixpoint 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. Sixpoint 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). Sixpoint 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. Sixpoint 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. Sixpoint 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. 
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 
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 
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 
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. 
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. 
Theme  Number 
Efficacy  32 
Appreciation  29 
Constructive Suggestions  16 
Theme  Number 
Efficacy  32 
Appreciation  29 
Constructive Suggestions  16 
Statement  Student's ttestp < 0.05?  MannWhitney Utest p < 0.05?  Effect Size (Cohen's d) 
G  Yes  Yes  1.10 
H  Yes  Yes  1.84 
Statement  Student's ttestp < 0.05?  MannWhitney Utest p < 0.05?  Effect Size (Cohen's d) 
G  Yes  Yes  1.10 
H  Yes  Yes  1.84 
[1] 
Changyan Di, Qingguo Zhou, Jun Shen, Li Li, Rui Zhou, Jiayin Lin. Innovation event model for STEM education: A constructivism perspective. STEM Education, 2021, 1 (1) : 6074. doi: 10.3934/steme.2021005 
[2] 
Sanjukta Hota, Folashade Agusto, Hem Raj Joshi, Suzanne Lenhart. Optimal control and stability analysis of an epidemic model with education campaign and treatment. Conference Publications, 2015, 2015 (special) : 621634. doi: 10.3934/proc.2015.0621 
[3] 
Yujuan Li, Robert N. Hibbard, Peter L. A. Sercombe, Amanda L. Kelk, ChengYuan Xu. Inspiring and engaging high school students with science and technology education in regional Australia. STEM Education, 2021, 1 (2) : 114126. doi: 10.3934/steme.2021009 
[4] 
Mingfeng Wang, Ruijun Liu, Chunsong Zhang, Zhao Tang. Daran robot, a reconfigurable, powerful, and affordable robotic platform for STEM education. STEM Education, 2021, 1 (4) : 299308. doi: 10.3934/steme.2021019 
[5] 
Sarai Hedges, Kim Given. Addressing confirmation bias in middle school data science education. Foundations of Data Science, 2022 doi: 10.3934/fods.2021035 
[6] 
Daniel Franco, Chris Guiver, Phoebe Smith, Stuart Townley. A switching feedback control approach for persistence of managed resources. Discrete & Continuous Dynamical Systems  B, 2021 doi: 10.3934/dcdsb.2021109 
[7] 
Sümeyra Uçar. Existence and uniqueness results for a smoking model with determination and education in the frame of nonsingular derivatives. Discrete & Continuous Dynamical Systems  S, 2021, 14 (7) : 25712589. doi: 10.3934/dcdss.2020178 
[8] 
Dragana Martinovic, Marina MilnerBolotin. Examination of modelling in K12 STEM teacher education: Connecting theory with practice. STEM Education, 2021, 1 (4) : 279298. doi: 10.3934/steme.2021018 
[9] 
Kevin G. Hare, Nikita Sidorov. Open maps: Small and large holes with unusual properties. Discrete & Continuous Dynamical Systems, 2018, 38 (11) : 58835895. doi: 10.3934/dcds.2018255 
[10] 
Andrea L. Bertozzi. Preface to special issue on mathematics of social systems. Discrete & Continuous Dynamical Systems  B, 2014, 19 (5) : iv. doi: 10.3934/dcdsb.2014.19.5i 
[11] 
Dongmei Zheng, Ercai Chen, Jiahong Yang. On large deviations for amenable group actions. Discrete & Continuous Dynamical Systems, 2016, 36 (12) : 71917206. doi: 10.3934/dcds.2016113 
[12] 
Yves Guivarc'h. On the spectrum of a large subgroup of a semisimple group. Journal of Modern Dynamics, 2008, 2 (1) : 1542. doi: 10.3934/jmd.2008.2.15 
[13] 
Anushaya Mohapatra, William Ott. Memory loss for nonequilibrium open dynamical systems. Discrete & Continuous Dynamical Systems, 2014, 34 (9) : 37473759. doi: 10.3934/dcds.2014.34.3747 
[14] 
Haiying Jing, Zhaoyu Yang. The impact of state feedback control on a predatorprey model with functional response. Discrete & Continuous Dynamical Systems  B, 2004, 4 (3) : 607614. doi: 10.3934/dcdsb.2004.4.607 
[15] 
Richard L Buckalew. Cell cycle clustering and quorum sensing in a response / signaling mediated feedback model. Discrete & Continuous Dynamical Systems  B, 2014, 19 (4) : 867881. doi: 10.3934/dcdsb.2014.19.867 
[16] 
Ruofeng Rao, Shouming Zhong. Inputtostate stability and noinputs stabilization of delayed feedback chaotic financial system involved in open and closed economy. Discrete & Continuous Dynamical Systems  S, 2021, 14 (4) : 13751393. doi: 10.3934/dcdss.2020280 
[17] 
Gary Froyland, Ognjen Stancevic. Escape rates and PerronFrobenius operators: Open and closed dynamical systems. Discrete & Continuous Dynamical Systems  B, 2010, 14 (2) : 457472. doi: 10.3934/dcdsb.2010.14.457 
[18] 
Gary Froyland, Philip K. Pollett, Robyn M. Stuart. A closing scheme for finding almostinvariant sets in open dynamical systems. Journal of Computational Dynamics, 2014, 1 (1) : 135162. doi: 10.3934/jcd.2014.1.135 
[19] 
Michael Schönlein. Computation of openloop inputs for uniformly ensemble controllable systems. Mathematical Control & Related Fields, 2021 doi: 10.3934/mcrf.2021046 
[20] 
Renato C. Calleja, Alessandra Celletti, Rafael de la Llave. Construction of response functions in forced strongly dissipative systems. Discrete & Continuous Dynamical Systems, 2013, 33 (10) : 44114433. doi: 10.3934/dcds.2013.33.4411 
Impact Factor:
Tools
Metrics
Other articles
by authors
[Back to Top]