Laura Toma
ABSTRACT
While previous research has investigated psychological factors in introductory programming courses, only little is known about their impact in algorithms courses. Similarly, despite the importance of collaborative problem solving in both academic and non-academic settings, only a small number of studies reports on group work in domains other than programming. In our case study, we focused on the labs of an introductory algorithms course. We measured the cognitive load of the lab assignments as well as the students' emotional reaction to them. We connect these observations to self-efficacy, performance, psychological traits, and help-seeking behavior as well as to the insights gained from a comprehensive set of follow-up interviews. Even though our study is a small-scale study, the results from applying both quantitative and qualitative methods frame directions for both pedagogic interventions and further (revalidation) studies related to the connection of non-cognitive factors, learning experiences, and performance in collaborative algorithms labs.
- S. Aldekhyl, R. B. Cavalcanti, and L. M. Naismith. Cognitive load predicts point-of-care ultrasound simulator performance. Perspectives in Medical Education, 7(1):23--32, Feb. 2018.Google Scholar
Cross Ref
- V. Aleven, E. Stahl, S. Schworm, F. Fischer, and R. Wallace. Help seeking and help design in interactive learning environments. Review of Educational Research, 73(3):277--320, 2003.Google ScholarCross Ref
- A. Bandura. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2):191--215, 1977.Google ScholarCross Ref
- L. Battestilli, A. Awasthi, and Y. Cao. Two-stage programming projects: Individual work followed by peer collaboration. In T. Barnes, D. D. Garcia, E. K. Hawthorne, and M. A. Pérez-Qui nones, editors, Proceedings of the 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018, pages 479--484. ACM Press, 2018. Google ScholarDigital Library
- J. Bennedsen and M. E. Caspersen. Optimists have more fun, but do they learn better? On the influence of emotional and social factors on learning introductory computer science. Computer Science Education, 18(1):1--16, 2008.Google ScholarCross Ref
- J. Berger, B. P. Cohen, and M. Zelditch, Jr. Status characteristics and social interaction. American Sociological Review, 37(3):241--255, June 1972.Google ScholarCross Ref
- S. Beyer. Why are women underrepresented in computer science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Computer Science Education, 24(2--3):153--192, 2014.Google Scholar
- S. Beyer. Women in computer science: Deterrents. In P. A. Laplante, editor, Encyclopedia of Computer Science and Technology, pages 871--879. CRC Press, second edition, 2017.Google ScholarCross Ref
- A. E. Black and E. L. Deci. The effects of instructors' autonomy support and students' autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84(6):740--756, Nov. 2000.Google ScholarCross Ref
- J. M. Blaney and J. G. Stout. Examining the relationship between introductory computing course experiences, self-efficacy, and belonging among first-generation college women. In M. E. Caspersen, S. H. Edwards, T. Barnes, and D. D. Garcia, editors, Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, pages 69--74. ACM Press, 2017. Google ScholarDigital Library
- S. S. Bomotti. Teaching assistant attitudes towards college teaching. The Review of Higher Education, 17(4):371--393, 1994.Google ScholarCross Ref
- M. Celepkolu and K. E. Boyer. Thematic analysis of students' reflections on pair programming in cs1. In T. Barnes, D. D. Garcia, E. K. Hawthorne, and M. A. Pérez-Qui nones, editors, Proceedings of the 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018, pages 771--776. ACM Press, 2018. Google ScholarDigital Library
- B. Chowdhury, A. C. Bart, and D. Kafura. Analysis of collaborative learning in a computational thinking class. In T. Barnes, D. D. Garcia, E. K. Hawthorne, and M. A. Pérez-Qui nones, editors, Proceedings of the 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018, pages 143--148. ACM Press, 2018. Google ScholarDigital Library
- J. Cohen. A power primer. Psychological Bulletin, 112(1):155--159, July 1992.Google ScholarCross Ref
- B. Coleman and M. Lang. Collaboration across the curriculum: A disciplined approach to developing team skills. In L. A. S. King, D. R. Musicant, T. Camp, and P. T. Tymann, editors, Proceedings of the 43rd ACM technical symposium on Computer science education, SIGCSE 2012, pages 277--282. ACM Press, 2012. Google ScholarDigital Library
- H. Danielsiek, P. Hubwieser, J. Krugel, J. Magenheim, L. Ohrndorf, D. Ossenschmidt, N. Schaper, and J. Vahrenhold. Undergraduate teaching assistants in computer science: Teaching-related beliefs, tasks, and competences. In Proceedings of the 2017 IEEE Global Engineering Education Conference (EDUCON), pages 718--725, 2017.Google ScholarCross Ref
- H. Danielsiek, W. Paul, and J. Vahrenhold. Detecting and understanding students' misconceptions related to algorithms and data structures. In L. A. S. King, D. R. Musicant, T. Camp, and P. T. Tymann, editors, Proceedings of the 43rd ACM technical symposium on Computer science education, SIGCSE 2012, pages 21--26. ACM Press, 2012. Google ScholarDigital Library
- H. Danielsiek, L. Toma, and J. Vahrenhold. An instrument to assess self-efficacy in introductory algorithms courses. In J. Tenenberg, D. Chinn, J. Sheard, and L. Malmi, editors, Proceedings of the 2017 ACM Conference on International Computing Education Research, ICER 2017, pages 257--265. ACM Press, 2017. Google ScholarDigital Library
- E. Deitrick, M. H. Wilkerson, and E. Simoneau. Understanding student collaboration in interdisciplinary computing activities. In J. Tenenberg, D. Chinn, J. Sheard, and L. Malmi, editors, Proceedings of the 2017 ACM Conference on International Computing Education Research, ICER 2017, pages 118--126. ACM Press, 2017. Google ScholarDigital Library
- P. Dillenbourg. What do you man by collaborative learning? In P. Dillenbourg, editor, Collaborative Learning: Cognitive and Computational Approaches, pages 1--19. Elsevier, Oxford, 1999.Google Scholar
- S. H. Edwards, N. Kandru, and M. B. M. Rajagopal. Investigating static analysis errors in student Java programs. In J. Tenenberg, D. Chinn, J. Sheard, and L. Malmi, editors, Proceedings of the 2017 ACM Conference on International Computing Education Research, ICER 2017, pages 65--72. ACM Press, 2017. Google ScholarDigital Library
- E. Enström and V. Kann. Iteratively intervening with the “most difficult” topics of an algorithms and complexity course. ACM Transactions on Computing Education, 17(1), Jan. 2017. Article 4, 38 pages. Google ScholarDigital Library
- F. J. Estrada and A. Tafliovich. Bridging the gap between desired and actual qualifications of teaching assistants: An experience report. In R. Davoli, M. Goldweber, G. Rößling, and I. Polycarpou, editors, Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2017, pages 134--139, New York City, 2017. ACM. Google ScholarDigital Library
- K. Falkner, N. J. G. Falkner, and R. Vivian. Collaborative learning and anxiety. In T. Camp, P. T. Tymann, J. D. Dougherty, and K. Nagel, editors, The 44th ACM Technical Symposium on Computer Science Education, SIGCSE '13, pages 227--232. ACM Press, 2013. Google ScholarDigital Library
- A. Fontana and J. H. Frey. The interview: From structured questions to negotiated text. In N. K. Denzin and Y. S. Lincoln, editors, Handbook of Qualitative Research, pages 645--672. SAGE Publications, second edition, 2000.Google Scholar
- J. Forbes, D. J. Malan, H. Pon-Barry, S. Reges, and M. Sahami. Scaling introductory courses using undergraduate teaching assistants. In M. E. Caspersen, S. H. Edwards, T. Barnes, and D. D. Garcia, editors, Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, pages 657--658. ACM Press, 2017. Google ScholarDigital Library
- A. Hellas, J. Leinonen, and P. Ihantola. Plagiarism in take-home exams: Help-seeking, collaboration, and systematic cheating. In R. Davoli, M. Goldweber, G. Rößling, and I. Polycarpou, editors, Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2017, pages 238--243, New York City, 2017. ACM. Google ScholarDigital Library
- T. Hübscher-Younger and N. H. Narayanan. Constructive and collaborative learning of algorithms. In S. Grissom, D. Knox, D. Joyce, and W. Dann, editors, Proceedings of the 34th SIGCSE Technical Symposium on Computer Science Education, pages 6--10. ACM Press, 2003. Google ScholarDigital Library
- S. B. Hulley, S. R. Cummings, W. S. Browner, D. G. Grady, and T. B. Newman. Designing Clinical Research. Lippincott Williams & Wilkins, fourth edition, 2013.Google Scholar
- M. Israel, Q. M. Wherfel, S. Shehab, E. A. Ramos, A. Metzger, and G. C. Reese. Assessing collaborative computing: development of the collaborative-computing observation instrument (C-COI). Computer Science Education, 26(2--3):208--233, 2016.Google Scholar
- P. Kinnunen and B. Simon. Experiencing programming assignments in CS1: the emotional toll. In M. E. Caspersen, M. J. Clancy, and K. Sanders, editors, Proceedings of the 6th International Workshop on Computing Education Research (ICER 2010), pages 77--86. ACM Press, 2010. Google ScholarDigital Library
- P. Kinnunen and B. Simon. CS majors' self-efficacy perceptions in CS1: Results in light of social cognitive theory. In M. E. Caspersen, A. Clear, and K. Sanders, editors, Proceedings of the 7th International Workshop on Computing Education Research (ICER 2011), pages 19--26. ACM Press, 2011. Google ScholarDigital Library
- E. Klieme and K. Reusser. Unterrichtsqualit"at und mathematisches Verst"andnis im internationalen Vergleich -- Ein Forschungsprojekt und erste Schritte zur Realisierung {Teaching quality and conceptual understanding in international comparison}. Unterrichtswissenschaft, 31(3):194--205, 2003. In German.Google Scholar
- A. N. Kumar. A study of stereotype threat in computer science. In T. Lapidot, J. Gal-Ezer, M. E. Caspersen, and O. Hazzan, editors, Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '12, pages 273--278, New York City, 2012. ACM. Google ScholarDigital Library
- J. Leppink and A. van den Heuvel. The evolution of cognitive load theory and its application to medical education. Perspectives in Medical Education, 4(3):119--127, June 2015.Google ScholarCross Ref
- C. M. Lewis and N. Shah. How equity and inequity can emerge in pair programming. In B. Dorn, J. Sheard, and Q. I. Cutts, editors, Proceedings of the eleventh annual International Conference on International Computing Education Research, ICER 2015, pages 41--50. ACM Press, 2015. Google ScholarDigital Library
- C. M. Lewis, N. Titterton, and M. Clancy. Using collaboration to overcome disparities in Java experience. In A. Clear, K. Sanders, and B. Simon, editors, International Computing Education Research Conference, ICER '12, pages 79--86. ACM Press, 2012. Google ScholarDigital Library
- A. Lishinski, A. Yadav, and R. Enbody. Students' emotional reactions to programming projects in introduction to programming: Measurement approach and influence on learning outcomes. In J. Tenenberg, D. Chinn, J. Sheard, and L. Malmi, editors, Proceedings of the 2017 ACM Conference on International Computing Education Research, ICER 2017, pages 30--38. ACM Press, 2017. Google ScholarDigital Library
- A. Lishinski, A. Yadav, J. Good, and R. Enbody. Learning to program: Gender differences and interactive effects of students' motivation, goals, and self-efficacy on performance. In J. Sheard, J. Tenenberg, D. Chinn, and B. Dorn, editors, Proceedings of the 2016 ACM Conference on International Computing Education Research (ICER 2016), pages 221--220. ACM Press, 2016. Google ScholarDigital Library
- J. L. Maples, L. Guan, N. T. Carter, and J. D. Miller. A test of the international personality item pool representation of the revised NEO personality inventory and development of a 120-item IPIP-based measure of the five-factor model. Psychological Assessment, 26(4):1070--1084, Dec. 2014.Google ScholarCross Ref
- R. Mason, Simon, G. Cooper, and B. Wilks. Flipping the assessment of cognitive load: Why and how. In J. Sheard, J. Tenenberg, D. Chinn, and B. Dorn, editors, Proceedings of the 2016 ACM Conference on International Computing Education Research, ICER 2016, pages 43--52. ACM Press, 2016. Google ScholarDigital Library
- B. B. Morrison. Dual modality code explanations for novices: Unexpected results. In J. Tenenberg, D. Chinn, J. Sheard, and L. Malmi, editors, Proceedings of the 2017 ACM Conference on International Computing Education Research, ICER 2017, pages 225--235. ACM Press, 2017. Google ScholarDigital Library
- B. B. Morrison, B. Dorn, and M. Guzdial. Measuring cognitive load in introductory CS: Adaptation of an instrument. In Q. I. Cutts, B. Simon, and B. Dorn, editors, International Computing Education Research Conference, ICER 2014, pages 131--138. ACM Press, 2014. Google ScholarDigital Library
- B. B. Morrison, L. E. Margulieux, and M. Guzdial. Subgoals, context, and worked examples in learning computing problem solving. In B. Dorn, J. Sheard, and Q. I. Cutts, editors, Proceedings of the eleventh annual International Conference on International Computing Education Research, ICER 2015, pages 21--29. ACM Press, 2015. Google ScholarDigital Library
- National Academies of Sciences Engineering and Medicine. Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments. The National Academies Press, Washington, DC, 2018. Google ScholarDigital Library
- M. Nelimarkka and A. Hellas. Social help-seeking strategies in a programming MOOC. In T. Barnes, D. D. Garcia, E. K. Hawthorne, and M. A. Pérez-Qui nones, editors, Proceedings of the 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018, pages 116--121. ACM Press, 2018. Google ScholarDigital Library
- L. Noaks and E. Wincup. Criminological Research: Understanding Qualitative Methods. SAGE Publications, 2004.Google ScholarCross Ref
- M. C. O'Connor and S. V. Paunonen. Big five personality predictors of post-secondary academic performance. Personality and Individual Differences, 43(5):971--990, 2007.Google ScholarCross Ref
- F. Pajares, Y. F. Cheon, and P. Oberman. Psychometric analysis of computer science help-seeking scales. Educational and Psychological Measurement, 64(3):496--513, 2004.Google ScholarCross Ref
- F. Pajares and D. H. Schunk. Self-beliefs and school success: Self-efficacy, self-concept, and school achievement. In R. Riding and S. Rayner, editors, Perception, pages 239--266. Ablex Publishing, London, 2001.Google Scholar
- E. Patitsas. A case study of the development of CS teaching assistants and their experiences with team teaching. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research, Koli Calling '13, pages 115--124, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- M. Prenzel, A. Kirsten, P. Dengler, and T. Beer. Selbstbestimmt motiviertes und interessiertes Lernen in der kaufm"annischen Erstausbildung. Zeitschrift fuer Berufs- und Wirtschaftsp"adagogik, Beiheft, 13:108--127, 1996. In German.Google Scholar
- T. W. Price, R. Zhi, and T. Barnes. Hint generation under uncertainty: The effect of hint quality on help-seeking behavior. In E. André, R. Baker, X. Hu, M. M. T. Rodrigo, and B. du Boulay, editors, Artificial Intelligence in Education, AIED 2017, volume 10331 of Lecture Notes in Computer Science, pages 311--322. Springer, 2017.Google ScholarCross Ref
- A. Quade. Development and validation of a computer science self-efficacy scale for CS0 courses and the group analysis of CS0 student self-efficacy. In Proceedings of the International Conference on Information Technology: Computers and Communications (ITCC'03), pages 60--64. IEEE Computer Society, 2003. Google ScholarDigital Library
- V. Ramalingam, D. LaBelle, and S. Wiedenbeck. Self-efficacy and mental models in learning to program. In R. D. Boyle, M. Clark, and A. Kumar, editors, Proceedings of the 9th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE 2004), pages 171--175, New York City, 2004. ACM. Google ScholarDigital Library
- V. Ramalingam and S. Wiedenbeck. Development and validation of scores on a computer programming self-efficacy scale and groups analyses of novice programmer self-efficacy. Journal of Educational Computing Research, 19(4):367--381, Dec. 1998.Google ScholarCross Ref
- F. J. Rodr'ıgues, K. M. Price, and K. E. Boyer. Exploring the pair programming process: Characteristics of effective collaboration. In M. E. Caspersen, S. H. Edwards, T. Barnes, and D. D. Garcia, editors, Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, pages 507--512. ACM Press, 2017. Google ScholarDigital Library
- M. Sandelowski. Sample size in qualitative research. Research in Nursing & Health, 18(2):179--183, Apr. 1995.Google ScholarCross Ref
- C. Seale. Quality in qualitative research. Qualitative Inquiry, 5(4):465--478, 1999.Google ScholarCross Ref
- R. Stebler and K. Reusser. Skalendokumentation der Schweizerischen Zusatzerhebungen zu TIMSS. Technical report, University of Zurich, Department of Educational Studies, 1995. In German.Google Scholar
- J. G. Stout, N. Dasgupta, M. Hunsinger, and M. A. McManus. STEMing the tide: Using ingroup experts to inoculate women's self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100(2):255--270, 2011.Google ScholarCross Ref
- J. Sweller. Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychological Review, 22(2):123--138, June 2010.Google ScholarCross Ref
- J. Sweller. Measuring cognitive load. Perspectives in Medical Education, 7(1):1--2, Feb. 2018.Google ScholarCross Ref
- I. A. G. Wilkinson and I. Y. Y. Fung. Small-group composition and peer effects. International Journal of Educational Research, 37(5):425--447, 2002.Google ScholarCross Ref
- J. Woehr. An interview with Donald Knuth. Dr Dobbs Journal, Apr. 1996. http://www.drdobbs.com/an-interview-with-donald-knuth/184409858.Google Scholar
- D. Zingaro. Peer instruction contributes to self-efficacy in CS1. In J. D. Dougherty, K. Nagel, A. Decker, and K. Eiselt, editors, Proceedings of the 45th SIGCSE Technical Symposium on Computer Science Education (SICGSE 2014), pages 373--378. ACM Press, 2014. Google ScholarDigital Library
Index Terms
- Self-Efficacy, Cognitive Load, and Emotional Reactions in Collaborative Algorithms Labs - A Case Study
Recommendations
An Instrument to Assess Self-Efficacy in Introductory Algorithms Courses
ICER '17: Proceedings of the 2017 ACM Conference on International Computing Education ResearchWe report on the development and validation of an instrument to assess self-efficacy in an introductory algorithms course. The instrument was designed based upon previous work by Ramalingam and Wiedenbeck and evaluated in a multi-institutional setup. We ...
The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning
Although research has suggested that group composition plays an important role in collaborative learning, the role of motivation in group composition has rarely been taken into account. This study investigates the effects of group composition of self-...
Improving Student Self-efficacy in CS 1 Using Examples of Erroneous Code
SIGCSE '19: Proceedings of the 50th ACM Technical Symposium on Computer Science EducationResearch has shown that self-efficacy, a student's confidence in his or her ability, contributes to student success. Here, we examine if teaching students to recognize common programming errors increases student self-efficacy in an introductory ...
Comments