Empirical research on pair programming in higher education: a literature review

References

• Ahmad, M., Abd Razak, A. H., Omar, M., Yasin, A., Romli, R., Abdul Mutalib, A., & Zahari, A. S. (2015). The impact of knowledge management in pair programming on program quality. In A. Abraham, A. K. Muda, and Y.-H. Choo (Eds.), Advances in intelligent systems and computing. Pattern analysis, intelligent security and the internet of things (pp. 159–168). Springer International Publishing. https://doi.org/10.1007/978-3-319-17398-6_15
   Google Scholar
• Al-Jarrah, A., Pontelli, E., Zaphiris, P., & Ioannou, A. (2016). On the effectiveness of a collaborative virtual pair-programming environment. In P. Zaphiris, and A. Ioannou (Eds.), Lecture notes in computer science. Learning and collaboration technologies (pp. 583–595). Springer. https://doi.org/10.1007/978-3-319-39483-1
   Google Scholar
• Alvarado, C., Lee, C. B., & Gillespie, G. (2014). New CS1 pedagogies and curriculum, the same success factors? In SIGCSE’14, Proceedings of the 45th ACM technical symposium on computer sciences education (pp. 379–384). Association for Computing Machinery. https://doi.org/10.1145/2538862.2538897
   Google Scholar
• Anderson, N., & Gegg-Harrison, T. (2013). Learning computer science in the ‘comfort zone of proximal development'. In SIGCSE ’13, Proceedings of the 44th ACM technical symposium on computer science education (pp. 495–500). Association for Computing Machinery. https://doi.org/10.1145/2445196.2445344
   Google Scholar
• Benadé, T., & Liebenberg, J. (2017). Pair programming as a learning method beyond the context of programming. In CSERC ’17, Proceedings of the 6th computer science education research conference (pp. 48–55). Association for Computing Machinery. https://doi.org/10.1145/3162087.3162098
   Google Scholar
• Boon, M. H., & Thomson, H. (2021). The effect direction plot revisited: Application of the 2019 Cochrane handbook guidance on alternative synthesis methods. Research Synthesis Methods, 12(1), 29–33. https://doi.org/10.1002/jrsm.1458
   PubMed Web of Science ®Google Scholar
• Bowers, C. A., Pharmer, J. A., & Salas, E. (2000). When member homogeneity is needed in work teams. Small Group Research, 31(3), 305–327. https://doi.org/10.1177/104649640003100303
   Web of Science ®Google Scholar
• Bowman, N. A., Jarratt, L., Culver, K. C., & Segre, A. M. (2019). How prior programming experience affects students’ pair programming experiences and outcomes. In ITiCSE ’19, Proceedings of the ACM conference on innovation and technology in computer science education 2019 (pp. 170–175). Association for Computing Machinery. https://doi.org/10.1145/3304221.3319781
   Google Scholar
• Bowman, N. A., Jarratt, L., Culver, K. C., & Segre, A. M. (2020). Pair programming in perspective: Effects on persistence, achievement, and equity in computer science. Journal of Research on Educational Effectiveness, 13(4), 1–28. https://doi.org/10.1080/19345747.2020.1799464
   Web of Science ®Google Scholar
• Braught, G., MacCormick, J., & Wahls, T. (2010). The benefits of pairing by ability. In SIGCSE ’10, Proceedings of the 41st ACM technical symposium on computer science education (pp. 249–253). Association for Computing Machinery. https://doi.org/10.1145/1734263.1734348
   Google Scholar
• Braught, G., Wahls, T., & Eby, L. M. (2011). The case for pair programming in the computer science classroom. ACM Transactions on Computing Education, 11(1), 1–21. https://doi.org/10.1145/1921607.1921609
   Google Scholar
• Bryant, S., Romero, P., & Du Boulay, B. (2006). The collaborative nature of pair programming. In P. Abrahamsson, M. Marchesi, & G. Succi (Eds.), Extreme programming and agile processes in software engineering (pp. 53–64). Springer. https://doi.org/10.1007/11774129_6
   Google Scholar
• Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., Welch, V., & Thomson, H. (2020). Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline. BMJ, 368, 1–6. http://dx.doi.org/10.1136/bmj.l6890
   Google Scholar
• Celepkolu, M., & Boyer, K. E. (2018a). The importance of producing shared code through pair programming. In SIGCSE ‘18, Proceedings of the 49th ACM technical symposium on computer science education (pp. 765–770). Association for Computing Machinery. https://doi.org/10.1145/3159450.3159506
   Google Scholar
• Celepkolu, M., & Boyer, K. E. (2018b). Thematic analysis of students’ reflections on pair programming in CS1. In SIGCSE ’18, Proceedings of the 49th ACM technical symposium on computer science education (pp. 771–776). Association for Computing Machinery. https://doi.org/10.1145/3159450.3159516
   Google Scholar
• Chen, K., & Rea, A. (2018). Do pair programming approaches transcend coding? Measuring agile attitudes in diverse information systems courses. Journal of Information Systems Education, 29(2), 53–64. http://jise.org/Volume29/n2/JISEv29n2p53.html
   Google Scholar
• Choi, K. S. (2013). Evaluating gender significance within a pair programming context. In HICSS ’13, Proceedings of the 46th Hawaii international conference on system sciences 2013 (pp. 4817–4825). IEEE Computer Society. https://doi.org/10.1109/HICSS.2013.209
   Google Scholar
• Coman, I. D., Robillard, P. N., Sillitti, A., & Succi, G. (2014). Cooperation, collaboration and pair-programming: Field studies on backup behavior. Journal of Systems and Software, 91, 124–134. https://doi.org/10.1016/j.jss.2013.12.037
   Web of Science ®Google Scholar
• Cukurova, M., Zhou, Q., Spikol, D., & Landolfi, L. (2020). Modelling collaborative problem-solving competence with transparent learning analytics: Is video data enough? In LAK´20, Proceedings of the 10th international conference on learning analytics & knowledge (LAK ‘20) (pp. 270–275). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375484
   Google Scholar
• de Oliveira, C. M. C., Canedo, E. D., Faria, H., Amaral, L. H. V., & Bonifácio, R. (2018). Improving student’s learning and cooperation skills using coding dojos (in the wild!). In FIE ‘18, Proceedings of the IEEE frontiers in education conference 2018 (pp. 1–8). IEEE. https://doi.org/10.1109/FIE.2018.8659056
   Google Scholar
• Demir, Ö., & Seferoglu, S. S. (2020a). A comparison of solo and pair programming in terms of flow experience, coding quality, and coding achievement. Journal of Educational Computing Research, 58(8), 1–19. https://doi.org/10.1177/0735633120949788
   Web of Science ®Google Scholar
• Demir, Ö., & Seferoglu, S. S. (2020b). The effect of determining pair programming groups according to various individual difference variables on group compatibility, flow, and coding performance. Journal of Educational Computing Research, 59(1), 1–30. https://doi.org/10.1177/0735633120949787
   Web of Science ®Google Scholar
• Dongo, T., Reed, A. H., & O’Hara, M. (2016). Exploring pair programming benefits for MIS majors. Journal of Information Technology Education-Innovations in Practice, 15, 223–239. https://doi.org/10.28945/3625
   Google Scholar
• Döring, N., & Bortz, J. (2016). Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. Springer. https://doi.org/10.1007/978-3-642-41089-5
   Google Scholar
• Dou, W., & He, W. (2010). Compatibility and requirements analysis of distributed pair programming. In ECTS ‘10, Proceedings of the 2nd international workshop on education technology and computer science, Wuhan, China (pp. 467–470). IEEE. https://doi.org/10.1109/ETCS.2010.367
   Google Scholar
• Dyba, T., Arisholm, E., Sjoberg, D. I. L., Hannay, J. E., & Shull, F. (2007). Are two heads better than one? On the effectiveness of pair programming. IEEE Software, 24(5), 12–15. http://dx.doi.org/10.1109/MS.2007.158
   Google Scholar
• Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). W. H. Freeman.
   Google Scholar
• Edwards, R. L., Stewart, J. K., & Ferati, M. (2010). Assessing the effectiveness of distributed pair programming for an online informatics curriculum. ACM Inroads, 1(1), 48–54. https://doi.org/10.1145/1721933.1721951
   Google Scholar
• Estácio, B., Oliveira, R., Marczak, S., Kalinowski, M., Garcia, A., Prikladnicki, R., & Lucena, C. (2015). Evaluating collaborative practices in acquiring programming skills: Findings of a controlled experiment. In SBES ‘15, Proceedings of the 29th brazilian symposium on software engineering 2015 (pp. 150–159). IEEE Computer Society. https://doi.org/10.1109/SBES.2015.24
   Google Scholar
• Estácio, B., Valentim, N., Rivero, L., Conte, T., & Prikladnicki, R. (2015). Evaluating the use of pair programming and coding dojo in teaching mockups development: An empirical study. In HICSS ‘15, Proceedings of the 48th Hawaii international conference 2015 (pp. 5084–5093). IEEE. https://doi.org/10.1109/HICSS.2015.602
   Google Scholar
• Faja, S. (2014). Evaluating effectiveness of pair programming as a teaching tool in programming courses. Information Systems Education Journal, 12(6), 36–45. https://eric.ed.gov/?id=EJ1140923
   Google Scholar
• Garousi, V., Giray, G., Tüzün, E., Catal, C., & Felderer, M. (2019). Aligning software engineering education with industrial needs: A meta-analysis. Journal of Systems and Software, 156, 65–83. https://doi.org/10.1016/j.jss.2019.06.044
   Web of Science ®Google Scholar
• Gerjets, P., & Scheiter, K. (2003). Goal configurations and processing strategies as moderators between instructional design and cognitive load: Evidence from hypertext-based instruction. Educational Psychologist, 38(1), 33–41. https://doi.org/10.1207/S15326985EP3801_5
   Web of Science ®Google Scholar
• Ghorashi, S., & Jensen, C. (2017). Integrating collaborative and live coding for distance education. Computer, 50(5), 27–35. https://doi.org/10.1109/MC.2017.131
   Web of Science ®Google Scholar
• Gillies, R. M., & Boyle, M. (2010). Teachers’ reflections on cooperative learning: Issues of implementation. Teaching and Teacher Education, 26(4), 933–940. https://doi.org/10.1016/j.tate.2009.10.034
   Web of Science ®Google Scholar
• Gillies, R. M., & Boyle, M. (2011). Teachers’ reflections of cooperative learning (CL): A two-year follow-up. Teaching Education, 22(1), 63–78. https://doi.org/10.1080/10476210.2010.538045
   Google Scholar
• Goel, S., & Kathuria, V. (2010). A novel approach for collaborative pair programming. Journal of Information Technology Education, 9, 183–196. https://doi.org/10.28945/1290
   Google Scholar
• Govender, D. W., & Govender, T. P. (2014). Using a collaborative learning technique as a pedagogic intervention for the effective teaching and learning of a programming course. Mediterranean Journal of Social Sciences, 5(20), 1077–1086. https://doi.org/10.5901/mjss.2014.v5n20p1077
   Google Scholar
• Hannay, J. E., Dybå, T., Arisholm, E., & Sjøberg, D. I. K. (2009). The effectiveness of pair programming: A meta-analysis. Information and Software Technology, 51(7), 1110–1122. https://doi.org/10.1016/j.infsof.2009.02.001
   Web of Science ®Google Scholar
• Harsley, R., Di Eugenio, B., Green, N., & Fossati, D. (2017). Enhancing an intelligent tutoring system to support student collaboration: Effects on learning and behavior. In E. Andre, R. Baker, X. Hu, M. M. T. Rodrigo, & B. DuBoulay (Eds.), Artificial intelligence in education (pp. 519–522). Springer. https://doi.org/10.1007/978-3-319-61425-0\_54
   Google Scholar
• Harsley, R., Fossati, D., Di Eugenio, B., & Green, N. (2017). Interactions of individual and pair programmers with an intelligent tutoring system for computer science. In SIGCSE ’17, Proceedings of the ACM SIGCSE technical symposium on computer science education 2017 (pp. 285–290). Association for Computing Machinery. https://doi.org/10.1145/3017680.3017786
   Google Scholar
• Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
   Web of Science ®Google Scholar
• Jarratt, L., Bowman, N. A., Culver, K. C., & Segre, A. M. (2019). A large-scale experimental study of gender and pair composition in pair programming. In ITiCSE ’19, Proceedings of the ACM conference on innovation and technology in computer science education 2019 (pp. 176–181). Association for Computing Machinery. https://doi.org/10.1145/3304221.3319782
   Google Scholar
• Jermann, P., & Dillenbourg, P. (2008). Group mirrors to support interaction regulation in collaborative problem solving. Computers & Education, 51(1), 279–296. https://doi.org/10.1016/j.compedu.2007.05.012
   Web of Science ®Google Scholar
• Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365–379. https://doi.org/10.3102/0013189X09339057
   Web of Science ®Google Scholar
• Jurado, F., Molina, A. I., Redondo, M. A., & Ortega, M. (2013). Cole-programming: Shaping collaborative learning support in eclipse. IEEE Revista Iberoamericana De Tecnologias Del Aprendizaje, 8(4), 153–162. https://doi.org/10.1109/RITA.2013.2284953
   Google Scholar
• Kaendler, C., Wiedmann, M., Rummel, N., & Spada, H. (2015). Teacher competencies for the implementation of collaborative learning in the classroom: A framework and research review. Educational Psychology Review, 27(3), 505–536. https://doi.org/10.1007/s10648-014-9288-9
   Web of Science ®Google Scholar
• Karthiekheyan, K., Ahmed, I., & Jayalakshmi, J. (2018). Pair programming for software engineering education: An empirical study. The International Arab Journal of Information Technology (IAJIT), 15(2), 246–255. https://doi.org/10.1109/TSE.2010.59
   Web of Science ®Google Scholar
• Kavitha, R. K., & Ahmed, M. S. I. (2015). Knowledge sharing through pair programming in learning environments: An empirical study. Education and Information Technologies, 20(2), 319–333. https://doi.org/10.1007/s10639-013-9285-5
   Web of Science ®Google Scholar
• Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering, Technical Report EBSE-2007-01, School of Computer Science and Mathematics, Keele University.
   Google Scholar
• Kongcharoen, C., Hwang, W.-Y., & Ghinea, G. (2017). Synchronized pair configuration in virtualization-based lab for learning computer networks. Educational Technology & Society, 20(3), 54–68. http://www.jstor.org/stable/26196119
   Google Scholar
• Kori, K., Pedaste, M., Leijen, Ä., & Tõnisson, E. (2016). The role of programming experience in ICT students’ learning motivation and academic achievement. International Journal of Information and Education Technology, 6(5), 331–337. https://doi.org/10.7763/IJIET.2016.V6.709
   Google Scholar
• Kori, K., Pedaste, M., Tonisson, E., Palts, T., Altin, H., Rantsus, R., Sell, R., Murtazin, K., & Ruutmann, T. (2015). First-year dropout in ICT studies. In EDUCON ‘15, Proceedings of IEEE global engineering education conference 2015 (pp. 437–445). IEEE. https://doi.org/10.1109/EDUCON.2015.7096008
   Google Scholar
• Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: A review of the research. Computers in Human Behavior, 19(3), 335–353. https://doi.org/10.1016/S0747-5632(02)00057-2
   Web of Science ®Google Scholar
• Lewis, C. M., Titterton, N., & Clancy, M. (2012). Using collaboration to overcome disparities in Java experience. In ICER ’12, Proceedings of the 9th annual international conference on international computing education research (pp. 79–86). Association for Computing Machinery. https://doi.org/10.1145/2361276.2361292
   Google Scholar
• Lou, Y., Abrami, P. C., & d’Apollonia, S. (2001). Small group and individual learning with technology: A meta-analysis. Review of Educational Research, 71(3), 449–521. https://doi.org/10.3102/00346543071003449
   Web of Science ®Google Scholar
• Maguire, P., Maguire, R., Hyland, P., & Marshall, P. (2014). Enhancing collaborative learning using pair programming: Who benefits? All Ireland Journal of Teaching and Learning in Higher Education, 6(2), 1411–1419. https://ojs.aishe.org/index.php/aishe-j/article/view/141
   Google Scholar
• Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. The American Psychologist, 59(1), 14–19. https://doi.org/10.1037/0003-066X.59.1.14
   PubMed Web of Science ®Google Scholar
• Mayer, R. E. (2011). Multimedia learning and games. In S. Tobias & J. D. Fletcher (Eds.), Computer games and instruction (pp. 281–305). Information Age Publishing.
   Google Scholar
• Mayer, R. E., & Johnson, C. I. (2010). Adding instructional features that promote learning in a game-like environment. Journal of Educational Computing Research, 42(3), 241–265. https://doi.org/10.2190/EC.42.3.a
   Web of Science ®Google Scholar
• McChesney, I. (2016). Three years of student pair programming: Action research insights and outcomes. In SIGCSE ’16, Proceedings of the 47th ACM technical symposium on computing science education (pp. 84–89). Association for Computing Machinery. https://doi.org/10.1145/2839509.2844565
   Google Scholar
• Nawahdah, M., & Taji, D. (2016). Investigating students’ behavior and code quality when applying pair-programming as a teaching technique in a Middle Eastern society. In EDUCON ‘16, Proceedings of IEEE global engineering education conference 2016 (pp. 32–39). IEEE. https://doi.org/10.1109/EDUCON.2016.7474527
   Google Scholar
• Nokes-Malach, T. J., Richey, J. E., & Gadgil, S. (2015). When is it better to learn together? Insights from research on collaborative learning. Educational Psychology Review, 27(4), 645–656. https://doi.org/10.1007/s10648-015-9312-8
   Web of Science ®Google Scholar
• O’Donnell, C., Buckley, J., Mahdi, A., Nelson, J., & English, M. (2015). Evaluating pair-programming for non-computer science major students. In SIGCSE ’15, Proceedings of the 46th ACM technical symposium on computer science education (pp. 569–574). Association for Computing Machinery. https://doi.org/10.1145/2676723.2677289
   Google Scholar
• Olivares, O. J. (2008). Collaborative vs. cooperative learning: The instructor’s role in computer supported collaborative learning. In K. L. Orvis & A. L. R. Lassiter (Eds.), Computer-supported collaborative learning: Best practices and principles for instructors (pp. 20–39). IGI global.
   Google Scholar
• Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., and Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(8284), n71. https://doi.org/10.1136/bmj.n71
   PubMedGoogle Scholar
• Radermacher, A., Walia, G., & Rummelt, R. (2012). Improving student learning outcomes with pair programming. In ICER ’12, Proceedings of the ninth annual international conference on international computing education research (pp. 87–92). Association for Computing Machinery. https://doi.org/10.1145/2361276.2361294
   Google Scholar
• Reckinger, S., & Hughes, B. (2020). Strategies for implementing in-class, active, programming assessments: A multi-level model. In SIGCSE ’20, Proceedings of the 51st ACM technical symposium on computer science education (pp. 454–460). Association for Computing Machinery. https://doi.org/10.1145/3328778.3366850
   Google Scholar
• Reigeluth, C. M. (1983). Instructional design: What is it and why is it. In C. M. Reigeluth (Ed.), Instructional-design theories and models: An overview of their current status (pp. 3–36). Erlbaum.
   Google Scholar
• Reigeluth, C. M., & Carr-Chellman, A. A. (2009). Understanding instructional theory. In C. M. Reigeluth & A. A. Carr-Chellman (Eds.), Instructional-design theories and models. Building a common knowledge base (pp. 3–26). Taylor and Francis.
   Google Scholar
• Rodrigo, M. M. T. (2017). Exploratory analysis of discourses between students engaged in a debugging task. In ICCE ‘17, Proceedings of the 25th international conference on computers in education (pp. 198–203). Asia-Pacific Society for Computers in Education.
   Google Scholar
• Rodriguez, F. J., & Boyer, K. E. (2015). Discovering individual and collaborative problem-solving modes with hidden Markov models. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Artificial intelligence in education (pp. 408–418). Springer. https://doi.org/10.1007/978-3-319-19773-9\_41
   Google Scholar
• Rodríguez, F. J., Price, K. M., & Boyer, K. E. (2017). Exploring the pair programming process: Characteristics of effective collaboration. In SIGCSE ’17, Proceedings of the ACM SIGCSE technical symposium on computer science education 2017 (pp. 507–512). Association for Computing Machinery. https://doi.org/10.1145/3017680.3017748
   Google Scholar
• Rong, G., Zhang, H., Xie, M., & Shao, D. (2012). Improving PSP education by pairing: An empirical study. In ICSE ’12, Proceedings of the 34th international conference on software engineering 2012 (pp. 1245–1254), IEEE. https://doi.org/10.1109/ICSE.2012.6227018
   Google Scholar
• Rummel, N., & Spada, H. (2005). Learning to collaborate: An instructional approach to promoting collaborative problem solving in computer-mediated settings. Journal of the Learning Sciences, 14(2), 201–241. https://doi.org/10.1207/s15327809jls1402_2
   Web of Science ®Google Scholar
• Ruys, I., Keer, H., & Aelterman, A. (2012). Examining pre-service teacher competence in lesson planning pertaining to collaborative learning. Journal of Curriculum Studies, 44(3), 349–379. https://doi.org/10.1080/00220272.2012.675355
   Web of Science ®Google Scholar
• Saab, N., van Joolingen, W. R., & van Hout-Wolters, B. H. A. (2007). Supporting communication in a collaborative discovery learning environment: The effect of instruction. Instructional Science, 35(1), 73–98. https://doi.org/10.1007/s11251-006-9003-4
   Web of Science ®Google Scholar
• Salleh, N., Mendes, E., & Grundy, J. (2011). Empirical studies of pair programming for CS/SE teaching in higher education: A systematic literature review. IEEE Transactions on Software Engineering, 37(4), 509–525. https://doi.org/10.1109/TSE.2010.59
   Web of Science ®Google Scholar
• Salleh, N., Mendes, E., & Grundy, J. (2014). Investigating the effects of personality traits on pair programming in a higher education setting through a family of experiments. Empirical Software Engineering, 19(3), 714–752. https://doi.org/10.1007/s10664-012-9238-4
   Web of Science ®Google Scholar
• Satratzemi, M., Xinogalos, S., Tsompanoudi, D., & Karamitopoulos, L. (2018). Examining student performance and attitudes on distributed pair programming. Scientific Programming, 2018, 1–8. https://doi.org/10.1155/2018/6523538
   Web of Science ®Google Scholar
• Schneider, M., & Preckel, F. (2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143(6), 565–600. https://doi.org/10.1037/bul0000098
   PubMed Web of Science ®Google Scholar
• Sennett, J., & Sherriff, M. (2010). Compatibility of partnered students in computer science education. In SIGCSE ’10, Proceedings of the 41st ACM technical symposium on computer science education (pp. 244–248). Association for Computing Machinery. https://doi.org/10.1145/1734263.1734347
   Google Scholar
• Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205
   Web of Science ®Google Scholar
• Tsompanoudi, D., Satratzemi, M., & Xinogalos, S. (2013). Exploring the effects of collaboration scripts embedded in a distributed pair programming system. In Proceedings of the 13th ACM/IEEE-CS joint conference on digital libraries (pp. 225–230). Association for Computing Machinery. https://doi.org/10.1145/2462476.2462500
   Google Scholar
• Tsompanoudi, D., Satratzemi, M., & Xinogalos, S. (2015). Distributed pair programming using collaboration scripts: An educational system and initial results. Informatics in Education, 14(2), 291–314. https://doi.org/10.15388/infedu.2015.17
   Google Scholar
• Tsompanoudi, D., Satratzemi, M., & Xinogalos, S. (2016). Evaluating the effects of scripted distributed pair programming on student performance and participation. IEEE Transactions on Education, 59(1), 24–31. https://doi.org/10.1109/TE.2015.2419192
   Web of Science ®Google Scholar
• Tsompanoudi, D., Satratzemi, M., Xinogalos, S., & Karamitopoulos, L. (2019). An empirical study on factors related to distributed pair programming. International Journal of Engineering Pedagogy, 9(2), 61–81. https://doi.org/10.3991/ijep.v9i2.9947
   Web of Science ®Google Scholar
• Umapathy, K., & Ritzhaupt, A. D. (2017). A meta-analysis of pair-programming in computer programming courses. ACM Transactions on Computing Education, 17(4), 1–13. https://doi.org/10.1145/2996201
   Web of Science ®Google Scholar
• Urai, T., Umezawa, T., & Osawa, N. (2015). Enhancements to support functions of distributed pair programming based on action analysis. In ITiCSE ’15, Proceedings of the ACM conference on innovation and technology in computer science education 2015 (pp. 177–182). Association for Computing Machinery. https://doi.org/10.1145/2729094.2742616
   Google Scholar
• van Boxtel, C., van der Linden, J., & Kanselaar, G. (2000). Collaborative learning tasks and the elaboration of conceptual knowledge. Learning and Instruction, 10(4), 311–330. https://doi.org/10.1016/S0959-4752(00)00002-5
   Web of Science ®Google Scholar
• van Dinter, R., Tekinerdogan, B., & Catal, C. (2021). Automation of systematic literature reviews: A systematic literature review. Information and Software Technology, 136, 106589. https://doi.org/10.1016/j.infsof.2021.106589
   Web of Science ®Google Scholar
• Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
   Web of Science ®Google Scholar
• Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), iii–xiii. http://www.jstor.org/stable/4132319
   Web of Science ®Google Scholar
• Xinogalos, S., Malliarakis, C., Tsompanoudi, D., & Satratzemi, M. (2015). Microworlds, games and collaboration: Three effective approaches to support novices in learning programming. In BCI ’15, Proceedings of the 7th Balkan conference on informatics conference (pp. 1–8). Association for Computing Machinery. https://doi.org/10.1145/2801081.2801094
   Google Scholar
• Xinogalos, S., Satratzemi, M., Chatzigeorgiou, A., & Tsompanoudi, D. (2017). Student perceptions on the benefits and shortcomings of distributed pair programming assignments. In EDUCON ‘17, Proceedings of the IEEE global engineering education conference 2017 (pp. 1512–1520), IEEE. https://doi.org/10.1109/EDUCON.2017.7943050
   Google Scholar
• Xinogalos, S., Satratzemi, M., Chatzigeorgiou, A., & Tsompanoudi, D. (2019). Factors affecting students’ performance in distributed pair programming. Journal of Educational Computing Research, 57(2), 513–544. https://doi.org/10.1177/0735633117749432
   Web of Science ®Google Scholar
• Xiong, Y., So, H. J., & Toh, Y. (2015). Assessing learners’ perceived readiness for computer-supported collaborative learning (CSCL): A study on initial development and validation. Journal of Computing in Higher Education, 27(3), 215–239. https://doi.org/10.1007/s12528-015-9102-9
   Web of Science ®Google Scholar
• Yang, Y.-F., Lee, C.-I., & Chang, C.-K. (2016). Learning motivation and retention effects of pair programming in data structures courses. Education for Information, 32(3), 249–267. https://doi.org/10.3233/EFI-160976
   Google Scholar
• Ying, K. M., Pezzullo, L. G., Ahmed, M., Crompton, K., Blanchard, J., & Boyer, K. E. (2019). In their own words: Gender differences in student perceptions of pair programming. In SIGCSE ’19, Proceedings of the 50th ACM technical symposium on computer science education (pp. 1053–1059). Association for Computing Machinery. https://doi.org/10.1145/3287324.3287380
   Google Scholar
• Zacharis, N. Z. (2011). Measuring the effects of virtual pair programming in an introductory programming Java course. IEEE Transactions on Education, 54(1), 168–170. https://doi.org/10.1109/TE.2010.2048328
   Web of Science ®Google Scholar
• Zarb, M., Hughes, J., & Richards, J. (2013). Industry-inspired guidelines improve students’ pair programming communication. In ITiCSE ’13, Proceedings of the 18th ACM conference on innovation and technology in computer science education (pp. 135–140). Association for Computing Machinery. https://doi.org/10.1145/2462476.2462504
   Google Scholar
• Zarb, M., Hughes, J., & Richards, J. (2014). Evaluating industry-inspired pair programming communication guidelines with undergraduate students. In SIGCSE’14, Proceedings of the 45th ACM technical symposium on computer sciences education (pp. 361–366). Association for Computing Machinery. https://doi.org/10.1145/2538862.2538980
   Google Scholar
• Zarb, M., Hughes, J., & Richards, J. (2015). Further evaluations of industry-inspired pair programming communication guidelines with undergraduate students. In SIGCSE ’15, Proceedings of the 46th ACM technical symposium on computer science education (pp. 314–319). Association for Computing Machinery. https://doi.org/10.1145/2676723.2677241
   Google Scholar
• Zhang, H., Babar, M. A., & Tell, P. (2011). Identifying relevant studies in software engineering. Information and Software Technology, 53(6), 625–637. https://doi.org/10.1016/j.infsof.2010.12.010
   Web of Science ®Google Scholar
• Zhang, Y., Yang, Y., Yang, Y., & Dou, W. (2017). Research on pair learning method and pattern based on pair programming. In CETCU 2017, Proceedings of the 3rd conference on education and teaching in colleges and universities (pp. 58–61), Atlantis Press. https://doi.org/10.2991/cetcu-17.2017.14
   Google Scholar