參考文獻 |
參考文獻
吳宥葶, 孫之元, & 李威儀. (2013). 大專院校開放式課程學習者之自我調節問卷研發與編製. 國立臺灣科技大學人文社會學報, 9(3), 189-208. https://doi.org/10.29506/JLASS
陳雅玲. (2021). 於混合學習中基於學習分析儀表板系統應用之自我調節學習方法. 數位學習科技期刊, 13(3), 27-42. https://doi.org/10.3966/2071260X2021071303002
Ackermann, E. (1996). Constructionist in practice: Designing, thinking, and learning in a digital world. Routledge.
Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24(2), 1527-1543. https://doi.org/10.1007/s10639-018-9839-7
Adekitan, A. I., & Salau, O. (2019). The impact of engineering students′ performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250. https://doi.org/10.1016/j.heliyon.2019.e01250
Agarwal, S. (2012). Data Mining in Education: Data Classification and Decision Tree Approach. International Journal of e-Education, e-Business, e-Management and e-Learning, 2(2), 140. https://doi.org/10.7763/ijeeee.2012.V2.97
Aguilar, S. J., Karabenick, S. A., Teasley, S. D., & Baek, C. (2021). Associations between learning analytics dashboard exposure and motivation and self-regulated learning. Computers & Education, 162, 104085. https://doi.org/10.1016/j.compedu.2020.104085
Ala-Mutka, K. M. (2005). A Survey of Automated Assessment Approaches for Programming Assignments. Computer Science Education, 15(2), 83-102. https://doi.org/10.1080/08993400500150747
Ali, L., Hatala, M., Gasevic, D., & Jovanovic, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470-489. https://doi.org/10.1016/j.compedu.2011.08.030
Almarabeh, H. (2017). Analysis of Students′ Performance by Using Different Data Mining Classifiers. International Journal of Modern Education and Computer Science, 9(8), 9-15. https://doi.org/10.5815/ijmecs.2017.08.02
Altadmri, A., & Brown, N. C. (2015). 37 million compilations: Investigating novice programming mistakes in large-scale student data. Proceedings of the 46th ACM technical symposium on computer science education, 522-527. https://doi.org/10.1145/2676723.2677258
Altintas, T., Gunes, A., & Sayan, H. (2016). A peer-assisted learning experience in computer programming language learning and developing computer programming skills. Innovations in Education and Teaching International, 53(3), 329-337. https://doi.org/10.1080/14703297.2014.993418
Ananiadou, K., & Claro, M. (2009). 21st Century Skills and Competences for New Millennium Learners in OECD Countries. OECD Education Working Papers, No. 41. OECD Publishing (NJ1). https://doi.org/10.1787/218525261154
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students′ performance using educational data mining. Computers & Education, 113, 177-194. https://doi.org/10.1016/j.compedu.2017.05.007
Astrachan, O., Hambrusch, S., Peckham, J., & Settle, A. (2009). The present and future of computational thinking. ACM SIGCSE Bulletin, 41(1), 549-550. https://doi.org/10.1145/1539024.1509053
Azevedo, A. (2019). Data Mining and Knowledge Discovery in Databases. In Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics (pp. 502-514). IGI Global. https://doi.org/10.4018/978-1-5225-7598-6.ch037
Baepler, P., & Murdoch, C. (2010). Academic Analytics and Data Mining in Higher Education. International Journal for the Scholarship of Teaching and Learning, 4(2). https://doi.org/10.20429/ijsotl.2010.040217
Baker, R. (2010). Data mining for education. International encyclopedia of education, 7(3), 112-118. https://doi.org/10.1016/B978-0-08-044894-7.01318-X
Baker, R., & Siemens, G. (2014). Learning analytics and educational data mining. Cambridge handbook of the leaning sciences (2nd edn). Cambridge University Press: New York, NY, 253-272. https://doi.org/10.1017/CBO9781139519526.016
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM| Journal of Educational Data Mining, 1(1), 3-17. https://doi.org/10.5281/zenodo.3554657
Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 23(1), 537-553. https://doi.org/10.1007/s10639-017-9616-z
Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S.-L. (2010). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1-6. https://doi.org/10.1016/j.iheduc.2008.10.005
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12. ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
Barrows, H. S., & Tamblyn, R. M. (1980). Problem-based learning: An approach to medical education (Vol. 1). Springer Publishing Company.
Beck, L., & Chizhik, A. (2013). Cooperative Learning Instructional Methods for CS1: Design, Implementation, and Evaluation. ACM Transactions on Computing Education, 13(3), 1-21. https://doi.org/10.1145/2492686
Bers, M. U. (2008). Blocks to Robots Learning with Technology in the Early Childhood Classroom. Teachers College Press, New York.
Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145-157. https://doi.org/10.1016/j.compedu.2013.10.020
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief. Office of Educational Technology, US Department of Education.
Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, M., & Rumble, M. (2012). Defining Twenty-First Century Skills. In Assessment and Teaching of 21st Century Skills (pp. 17-66). Springer. https://doi.org/10.1007/978-94-007-2324-5_2
Bjögvinsson, E., Ehn, P., & Hillgren, P.-A. (2012). Design Things and Design Thinking: Contemporary Participatory Design Challenges. Design Issues, 28(3), 101-116. https://doi.org/10.1162/DESI_a_00165
Blikstein, P. (2011). Using learning analytics to assess students′ behavior in open-ended programming tasks. Proceedings of the 1st international conference on learning analytics and knowledge, 110-116. https://doi.org/10.1145/2090116.2090132
Bodily, R., & Verbert, K. (2017). Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems. IEEE Transactions on Learning Technologies, 10(4), 405-418. https://doi.org/10.1109/Tlt.2017.2740172
Bossert, S. T. (2016). Chapter 6: Cooperative Activities in the Classroom. Review of Research in Education, 15(1), 225-250. https://doi.org/10.3102/0091732x015001225
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada, 1, 25. http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf
Bull, G., Garofalo, J., & Hguyen, N. R. (2020). Thinking about computational thinking. Journal of Digital Learning in Teacher Education, 36(1), 6-18. https://doi.org/10.1080/21532974.2019.1694381
Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67-69. http://www.inf.ed.ac.uk/publications/online/1245.pdf
Butler, D. L., & Winne, P. H. (1995). Feedback and Self-Regulated Learning - a Theoretical Synthesis. Review of Educational Research, 65(3), 245-281. https://doi.org/10.3102/00346543065003245
Chang, C. S., Chung, C. H., & Chang, J. A. (2020). Influence of problem-based learning games on effective computer programming learning in higher education. Etr&D-Educational Technology Research and Development, 68(5), 2615-2634. https://doi.org/10.1007/s11423-020-09784-3
Chang, Y.-H., Song, A.-C., & Fang, R.-J. (2018). Integrating ARCS Model of Motivation and PBL in Flipped Classroom: a Case Study on a Programming Language. EURASIA Journal of Mathematics, Science and Technology Education, 14(12). https://doi.org/10.29333/ejmste/97187
Chao, J. Y., Tzeng, P. W., & Po, H. Y. (2016). The Study of Problem Solving Process of E-book PBL Course of Atayal Senior High School Students in Taiwan. EURASIA Journal of Mathematics, Science and Technology Education, 13(3), 1001-1012. https://doi.org/10.12973/eurasia.2017.00654a
Chen, C. H., & Su, C. Y. (2019). Using the BookRoll E-Book System to Promote Self-Regulated Learning, Self-Efficacy and Academic Achievement for University Students. Educational Technology & Society, 22(4), 33-46. https://www.jstor.org/stable/26910183
Chen, C. Y., & Teng, K. C. (2011). The design and development of a computerized tool support for conducting senior projects in software engineering education. Computers & Education, 56(3), 802-817. https://doi.org/10.1016/j.compedu.2010.10.022
Chis, A. E., Moldovan, A. N., Murphy, L., Pathak, P., & Muntean, C. H. (2018). Investigating Flipped Classroom and Problem-based Learning in a Programming Module for Computing Conversion Course. Educational Technology & Society, 21(4), 232-247. https://www.jstor.org/stable/26511551
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29. https://doi.org/10.1109/tlt.2016.2616312
Corrin, L., & De Barba, P. (2015). How do students interpret feedback delivered via dashboards? https://doi.org/10.1145/2723576.2723662
da Silva Cintra, C., & Bittencourt, R. A. (2015). Being a PBL teacher in computer engineering: an interpretative phenomenological analysis. 2015 IEEE Frontiers in Education Conference (FIE), 1-8. https://doi.org/10.1109/FIE.2015.7344234
Daly, J. E. (2009). Special Issue. Journal of Educational Technology Systems, 37(3), 247-249. https://doi.org/10.2190/ET.37.3.a
Delisle, R. (1997). How to use problem-based learning in the classroom. Ascd.
Dembo, M. H., & Eaton, M. J. (2000). Self-regulation of academic learning in middle-level schools. Elementary School Journal, 100(5), 473-490. https://doi.org/10.1086/499651
dos Santos, S. C., Reis, P. B. S., Reis, J. F. S., & Tavares, F. (2021). Two Decades of PBL in Teaching Computing: A Systematic Mapping Study. IEEE Transactions on Education, 64(3), 233-244. https://doi.org/10.1109/te.2020.3033416
Duke, R., Salzman, E., Burmeister, J., Poon, J., & Murray, L. (2000). Teaching programming to beginners-choosing the language is just the first step. Proceedings of the Australasian conference on Computing education, 79-86. https://doi.org/10.1145/359369.359381
Durall, E., & Gros, B. (2014). Learning Analytics as a Metacognitive Tool. CSEDU (1), 380-384. https://doi.org/10.5220/0004933203800384
Edens, K. M. (2010). Preparing Problem Solvers for the 21st Century through Problem-Based Learning. College Teaching, 48(2), 55-60. https://doi.org/10.1080/87567550009595813
English, M. C., & Kitsantas, A. (2013). Supporting Student Self-Regulated Learning in Problem- and Project-Based Learning. Interdisciplinary Journal of Problem-Based Learning, 7(2), 6. https://doi.org/10.7771/1541-5015.1339
Ferguson, R., & Shum, S. B. (2011). Learning analytics to identify exploratory dialogue within synchronous text chat. Proceedings of the 1st international conference on learning analytics and knowledge, 99-103. https://doi.org/10.1145/2090116.2090130
Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-Regulated Learning strategies using e-learning tools for Computer Science. Computers & Education, 123, 150-163. https://doi.org/10.1016/j.compedu.2018.05.006
García-Peñalvo, F. J., & Mendes, A. J. (2018). Exploring the computational thinking effects in pre-university education. Computers in Human Behavior, 80, 407-411. https://doi.org/10.1016/j.chb.2017.12.005
Glick, D., Cohen, A., Festinger, E., Xu, D., Li, Q., & Warschauer, M. (2019). Predicting Success, Preventing Failure. In Utilizing Learning Analytics to Support Study Success (pp. 249-273). Springer. https://doi.org/10.1007/978-3-319-64792-0_14
Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012). The student activity meter for awareness and self-reflection. In CHI′12 Extended Abstracts on Human Factors in Computing Systems (pp. 869-884). https://doi.org/10.1145/2212776.2212860
Gravill, J., & Compeau, D. (2008). Self-regulated learning strategies and software training. Information & Management, 45(5), 288-296. https://doi.org/10.1016/j.im.2008.03.001
Greening, T., Kay, J., Kingston, J. H., & Crawford, K. (1996). Problem-based learning of first year computer science. Proceedings of the 1st Australasian conference on Computer science education, 13-18. https://doi.org/10.1145/369585.369588
Grout, V., & Houlden, N. (2014). Taking Computer Science and Programming into Schools: The Glyndŵr/BCS Turing Project. Procedia - Social and Behavioral Sciences, 141, 680-685. https://doi.org/10.1016/j.sbspro.2014.05.119
Grover, S., & Pea, R. (2013). Computational Thinking in K-12: A Review of the State of the Field. Educational Researcher, 42(1), 38-43. https://doi.org/10.3102/0013189x12463051
Gupta, S., & Dubey, S. K. (2012). Automatic assessment of programming assignment. Computer Science & Engineering, 2(1), 67. https://doi.org/10.5121/csit.2012.2129
Hadwin, A., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. https://doi.org/10.4324/9781315697048-6
Hadwin, A., & Oshige, M. (2011). Self-Regulation, Coregulation, and Socially Shared Regulation: Exploring Perspectives of Social in Self-Regulated Learning Theory. Teachers College Record, 113(2), 240-264. https://doi.org/10.1177/016146811111300204
Henderson, P. B., Cortina, T. J., & Wing, J. M. (2007). Computational thinking. Proceedings of the 38th SIGCSE technical symposium on Computer science education, 195-196. https://doi.org/10.1145/1227310.1227378
Hmelo-Silver, C. E. (2004). Problem-Based Learning: What and How Do Students Learn? Educational Psychology Review, 16(3), 235-266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
Hmelo, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing to learn about complex systems. Journal of the Learning Sciences, 9(3), 247-298. https://doi.org/10.1207/S15327809jls0903_2
Hooshyar, D., Kori, K., Pedaste, M., & Bardone, E. (2019). The potential of open learner models to promote active thinking by enhancing self-regulated learning in online higher education learning environments. British Journal of Educational Technology, 50(5), 2365-2386. https://doi.org/10.1111/bjet.12826
Hou, X., Yang, H.-b., & Liu, J.-b. (2010). A problem-based teaching method in XML course. 2010 5th International Conference on Computer Science & Education, 399-402. https://doi.org/10.1109/ICCSE.2010.5593600
Hussain, S., Abdulaziz Dahan, N., Ba-Alwi, F. M., & Ribata, N. (2018). Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447-459. https://doi.org/10.11591/ijeecs.v9.i2.pp447-459
Hwang, G. J., Wang, S. Y., & Lai, C. L. (2021). Effects of a social regulation-based online learning framework on students′ learning achievements and behaviors in mathematics. Computers & Education, 160. https://doi.org/10.1016/j.compedu.2020.104031
Ifenthaler, D. (2020). Change Management for Learning Analytics. In Artificial Intelligence Supported Educational Technologies (pp. 261-272). Springer. https://doi.org/10.1007/978-3-030-41099-5_15
Ifenthaler, D., & Gibson, D. (2020). Adoption of data analytics in higher education learning and teaching. Springer.
Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared metacognition of dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 21(3), 379-393. https://doi.org/10.1016/j.learninstruc.2010.05.002
Ioannidou, A., Bennett, V., Repenning, A., Koh, K. H., & Basawapatna, A. (2011). Computational Thinking Patterns. Online Submission. http://files.eric.ed.gov/fulltext/ED520742.pdf
Jarvenoja, H., & Jarvela, S. (2009). Emotion control in collaborative learning situations: do students regulate emotions evoked by social challenges? Br J Educ Psychol, 79(Pt 3), 463-481. https://doi.org/10.1348/000709909X402811
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. European Conference on Technology Enhanced Learning, 82-96. https://doi.org/10.1007/978-3-319-66610-5_7
Järvelä, S., & Hadwin, A. F. (2013). New Frontiers: Regulating Learning in CSCL. Educational Psychologist, 48(1), 25-39. https://doi.org/10.1080/00461520.2012.748006
Järvelä, S., Järvenoja, H., & Näykki, P. (2013). Analyzing Regulation of Motivation as an Individual and Social Process: A Situated Approach. In Interpersonal Regulation of Learning and Motivation (pp. 184-201). Routledge. https://doi.org/10.4324/9780203117736-15
Järvelä, S., Kirschner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., Koivuniemi, M., & Järvenoja, H. (2014). Enhancing socially shared regulation in collaborative learning groups: designing for CSCL regulation tools. Educational Technology Research and Development, 63(1), 125-142. https://doi.org/10.1007/s11423-014-9358-1
Kalaian, S. A., & Kasim, R. M. (2015). Small-Group vs. Competitive Learning in Computer Science Classrooms. In Innovative Teaching Strategies and New Learning Paradigms in Computer Programming (pp. 46-64). IGI Global. https://doi.org/10.4018/978-1-4666-7304-5.ch003
Kalelioglu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200-210. https://doi.org/10.1016/j.chb.2015.05.047
Kaufman, D. M., & Mann, K. V. (1996). Comparing students′ attitudes in problem-based and conventional curricula. Acad Med, 71(10), 1096-1099. https://doi.org/10.1097/00001888-199610000-00018
Kay, J., & Bull, S. (2015). New opportunities with open learner models and visual learning analytics. International Conference on Artificial Intelligence in Education, 666-669. https://doi.org/10.1007/978-3-319-19773-9_87
Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. Acm Computing Surveys, 37(2), 83-137. https://doi.org/10.1145/1089733.1089734
Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women′s university in South Korea. Computers & Education, 127, 233-251. https://doi.org/10.1016/j.compedu.2018.08.023
Kim, J., Jo, I. H., & Park, Y. (2016). Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review, 17(1), 13-24. https://doi.org/10.1007/s12564-015-9403-8
Kizilcec, R. F., & Halawa, S. (2015). Attrition and achievement gaps in online learning. Proceedings of the second (2015) ACM conference on learning@ scale, 57-66. https://doi.org/10.1145/2724660.2724680
Kizilcec, R. F., Perez-Sanagustin, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18-33. https://doi.org/10.1016/j.compedu.2016.10.001
Korkmaz, Ö. (2012). A validity and reliability study of the Online Cooperative Learning Attitude Scale (OCLAS). Computers & Education, 59(4), 1162-1169. https://doi.org/10.1016/j.compedu.2012.05.021
Kostopoulos, G., Kotsiantis, S., Pierrakeas, C., Koutsonikos, G., & Gravvanis, G. A. (2018). Forecasting students′ success in an open university. International Journal of Learning Technology, 13(1), 26-43. https://doi.org/10.1504/ijlt.2018.091630
Kuo, H. C., Yang, Y. T. C., Chen, J. S., Hou, T. W., & Ho, M. T. (2022). The Impact of Design Thinking PBL Robot Course on College Students′ Learning Motivation and Creative Thinking. IEEE Transactions on Education, 65(2), 124-131. https://doi.org/10.1109/Te.2021.3098295
Lajis, A., Baharudin, S. A., Ab Kadir, D., Ralim, N. M., Nasir, H. M., & Aziz, N. A. (2018). A review of techniques in automatic programming assessment for practical skill test. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-5), 109-113. https://jtec.utem.edu.my/jtec/article/view/4394/3251
Liang, Y., Liu, Q., Xu, J., & Wang, D. (2009). The recent development of automated programming assessment. 2009 International Conference on Computational Intelligence and Software Engineering, 1-5. https://doi.org/10.1109/CISE.2009.5365307
Lin, J. W., & Lai, Y. C. (2013). Harnessing Collaborative Annotations on Online Formative Assessments. Educational Technology & Society, 16(1), 263-274. https://www.jstor.org/stable/pdf/jeductechsoci.16.1.263.pdf
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing Pedagogical Action: Aligning Learning Analytics With Learning Design. American Behavioral Scientist, 57(10), 1439-1459. https://doi.org/10.1177/0002764213479367
Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51-61. https://doi.org/10.1016/j.chb.2014.09.012
Lykke, M., Coto, M., Mora, S., Vandel, N., & Jantzen, C. (2014). Motivating programming students by problem based learning and LEGO robots. 2014 IEEE Global Engineering Education Conference (EDUCON), 544-555. https://doi.org/10.1109/EDUCON.2014.6826146
Manovich, L. (2013). Software takes command (Vol. 5). A&C Black.
McCrudden, M. T., & Schraw, G. (2007). Relevance and goal-focusing in text processing. Educational Psychology Review, 19(2), 113-139. https://doi.org/10.1007/s10648-006-9010-7
Mergendoller, J. R., Maxwell, N. L., & Bellisimo, Y. (2006). The Effectiveness of Problem-Based Instruction: A Comparative Study of Instructional Methods and Student Characteristics. Interdisciplinary Journal of Problem-Based Learning, 1(2), 5. https://doi.org/10.7771/1541-5015.1026
Michalsky, T., & Schechter, C. (2013). Preservice teachers′ capacity to teach self-regulated learning: Integrating learning from problems and learning from successes. Teaching and Teacher Education, 30, 60-73. https://doi.org/10.1016/j.tate.2012.10.009
Michel, C., Lavoué, E., & Piétrac, L. (2012). A dashboard to regulate project-based learning. European Conference on Technology Enhanced Learning, 250-263. https://doi.org/10.1007/978-3-642-33263-0_20
Mimis, M., El Hajji, M., Es-saady, Y., Oueld Guejdi, A., Douzi, H., & Mammass, D. (2019). A framework for smart academic guidance using educational data mining. Education and Information Technologies, 24(2), 1379-1393. https://doi.org/10.1007/s10639-018-9838-8
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: a model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199-218. https://doi.org/10.1080/03075070600572090
Norris, D., Baer, L., Leonard, J., Pugliese, L., & Lefrere, P. (2008). Action analytics: Measuring and improving performance that matters in higher education. EDUCAUSE review, 43(1), 42. https://www.academia.edu/download/46259642/Action_Analytics_Norris_Lefrere_ERM0813.pdf
Nuutila, E., Törmä, S., & Malmi, L. (2005). PBL and computer programming—the seven steps method with adaptations. Computer Science Education, 15(2), 123-142. https://doi.org/10.1080/08993400500150788
Oliver, R. (1993). Measuring Hierarchical Levels of Programming Knowledge. Journal of Educational Computing Research, 9(3), 299-312. https://doi.org/10.2190/0lgx-M45x-2wbk-B7a6
Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3-12. https://www.econstor.eu/bitstream/10419/193806/1/econ-review-v10-i1-p003-012.pdf
Panadero, E., & Jarvela, S. (2015). Socially Shared Regulation of Learning: A Review. European Psychologist, 20(3), 190-203. https://doi.org/10.1027/1016-9040/a000226
Pardo, A., Poquet, O., Martínez-Maldonado, R., & Dawson, S. (2017). Provision of data-driven student feedback in la & EDM. Handbook of learning analytics, 163-174. https://doi.org/10.18608/hla17.014
Park, Y., & Jo, I.-H. (2019). Factors that affect the success of learning analytics dashboards. Educational Technology Research and Development, 67(6), 1547-1571. https://doi.org/10.1007/s11423-019-09693-0
Peng, W. (2010). Practice and experience in the application of problem-based learning in computer programming course. 2010 International Conference on Educational and Information Technology, 1, V1-170-V171-172. https://doi.org/10.1109/ICEIT.2010.5607778
Podgorelec, V., & Kuhar, S. (2011). Taking Advantage of Education Data: Advanced Data Analysis and Reporting in Virtual Learning Environments. Elektronika Ir Elektrotechnika, 114(8), 111-116. https://doi.org/10.5755/j01.eee.114.8.708
Risemberg, R., & Zimmerman, B. J. (1992). Self‐regulated learning in gifted students. Roeper Review, 15(2), 98-101. https://doi.org/10.1080/02783199209553476
Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7-12. https://doi.org/10.18608/jla.2015.21.2
Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 40(6), 601-618. https://doi.org/10.1109/Tsmcc.2010.2053532
Romero, M., Laferriere, T., & Power, T. M. (2016). The Move is On! From the Passive Multimedia Learner to the Engaged Co-creator. eLearn, 2016(3). https://doi.org/10.1145/2904374.2893358
Romli, R., Sulaiman, S., & Zamli, K. Z. (2015). Improving Automated Programming Assessments: User Experience Evaluation Using FaSt-generator. Procedia Computer Science, 72, 186-193. https://doi.org/10.1016/j.procs.2015.12.120
Roy, S., & Garg, A. (2017). Predicting academic performance of student using classification techniques. 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), 568-572. https://doi.org/10.1109/UPCON.2017.8251112
Salomon, G., & Globerson, T. (1987). Skill may not be enough: The role of mindfulness in learning and transfer. International Journal of Educational Research, 11(6), 623-637. https://doi.org/10.1016/0883-0355(87)90006-1
Salovaara, H. (2005). An exploration of students′ strategy use in inquiry-based computer-supported collaborative learning. Journal of Computer Assisted Learning, 21(1), 39-52. https://doi.org/10.1111/j.1365-2729.2005.00112.x
Sampson, V., & Clark, D. (2009). The impact of collaboration on the outcomes of scientific argumentation. Science Education, 93(3), 448-484. https://doi.org/10.1002/sce.20306
Schmidt, H. G., Vermeulen, L., & van der Molen, H. T. (2006). Longterm effects of problem-based learning: a comparison of competencies acquired by graduates of a problem-based and a conventional medical school. Med Educ, 40(6), 562-567. https://doi.org/10.1111/j.1365-2929.2006.02483.x
Schmitz, B. (2001). Self-Monitoring zur Unterstützung des Transfers einer Schulung in Selbstregulation für Studierende. Zeitschrift für Pädagogische Psychologie, 15(3/4), 181-197. https://doi.org/10.1024//1010-0652.15.34.181
Schoor, C., & Bannert, M. (2012). Exploring regulatory processes during a computer-supported collaborative learning task using process mining. Computers in Human Behavior, 28(4), 1321-1331. https://doi.org/10.1016/j.chb.2012.02.016
Shaw, R. S. (2013). The relationships among group size, participation, and performance of programming language learning supported with online forums. Computers & Education, 62, 196-207. https://doi.org/10.1016/j.compedu.2012.11.001
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
Siemens, G. (2012). Learning analytics: envisioning a research discipline and a domain of practice. Proceedings of the 2nd international conference on learning analytics and knowledge, 4-8. https://doi.org/10.1145/2330601.2330605
Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851
Siemens, G., & Baker, R. S. d. (2012). Learning analytics and educational data mining: towards communication and collaboration. Proceedings of the 2nd international conference on learning analytics and knowledge, 252-254. https://doi.org/10.1145/2330601.2330661
Siemens, G., & Gasevic, D. (2012). Guest Editorial - Learning and Knowledge Analytics. Educational Technology & Society, 15(3), 1-2. https://www.researchgate.net/profile/Mohamed-Mourad-Lafifi/post/Could_anybody_point_out_good_references_book_or_other_manual_about_SIEMENS_FUM_Card_such_as_FUM_230_FUM_511_and_son_on/attachment/59d64c3f79197b80779a6180/AS%3A484047265767424%401492417269115/download/Journal+of+Educational+Technology+%26+Society.pdf#page=6
Song, D., Hong, H., & Oh, E. Y. (2021). Applying computational analysis of novice learners? computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in Human Behavior, 120, 106746. https://doi.org/10.1016/j.chb.2021.106746
Souza, S. M., & Bittencourt, R. A. (2019). Motivation and engagement with pbl in an introductory programming course. 2019 IEEE Frontiers in Education Conference (FIE), 1-9. https://doi.org/10.1109/FIE43999.2019.9028419
Souza, S. M., & Bittencourt, R. A. (2020). Report of a CS1 Course for Computer Engineering Majors Based on PBL. 2020 IEEE Global Engineering Education Conference (EDUCON), 837-846. https://doi.org/10.1109/EDUCON45650.2020.9125121
Spector, J. M. (2015). The SAGE encyclopedia of educational technology. Sage Publications. https://doi.org/10.4135/9781483346397.n112
Splichal, J. M., Oshima, J., & Oshima, R. (2018). Regulation of collaboration in project-based learning mediated by CSCL scripting reflection. Computers & Education, 125, 132-145. https://doi.org/10.1016/j.compedu.2018.06.003
Stevenson, M. P., Hartmeyer, R., & Bentsen, P. (2017). Systematically reviewing the potential of concept mapping technologies to promote self-regulated learning in primary and secondary science education. Educational Research Review, 21, 1-16. https://doi.org/10.1016/j.edurev.2017.02.002
Tambouris, E., Panopoulou, E., Tarabanis, K., Ryberg, T., Buus, L., Peristeras, V., Lee, D., & Porwol, L. (2012). Enabling Problem Based Learning through Web 2.0 Technologies: PBL 2.0. Educational Technology & Society, 15(4), 238-251. https://www.jstor.org/stable/pdf/jeductechsoci.15.4.238.pdf
Tan, P.-N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education India. https://www-users.cse.umn.edu/~kumar001/dmbook/dmsol_11_07_2021.pdf
Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://doi.org/10.1016/j.compedu.2019.103798
Ullah, Z., Lajis, A., Jamjoom, M., Altalhi, A., Al-Ghamdi, A., & Saleem, F. (2018). The effect of automatic assessment on novice programming: Strengths and limitations of existing systems. Computer Applications in Engineering Education, 26(6), 2328-2341. https://doi.org/10.1002/cae.21974
Unal, A., & Topu, F. B. (2021). Effects of teaching a computer programming language via hybrid interface on anxiety, cognitive load level and achievement of high school students. Education and Information Technologies, 26(5), 5291-5309. https://doi.org/10.1007/s10639-021-10536-w
Vauras, M., Iiskala, T., Kajamies, A., Kinnunen, R., & Lehtinen, E. (2003). Shared-regulation and motivation of collaborating peers: A case analysis. Psychologia, 46(1), 19-37. https://doi.org/10.2117/psysoc.2003.19
Veenman, M. V. J. (2013). Assessing Metacognitive Skills in Computerized Learning Environments. In International handbook of metacognition and learning technologies (pp. 157-168). Springer. https://doi.org/10.1007/978-1-4419-5546-3_11
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning Analytics Dashboard Applications. American Behavioral Scientist, 57(10), 1500-1509. https://doi.org/10.1177/0002764213479363
Volet, S., Vauras, M., & Salonen, P. (2009). Self- and Social Regulation in Learning Contexts: An Integrative Perspective. Educational Psychologist, 44(4), 215-226. https://doi.org/10.1080/00461520903213584
von Matt, u. (1994). Kassandra. ACM SIGCUE Outlook, 22(1), 26-40. https://doi.org/10.1145/182107.182101
Wang, Q. Y., & Woo, H. L. (2007). Comparing asynchronous online discussions and face-to-face discussions in a classroom setting. British Journal of Educational Technology, 38(2), 272-286. https://doi.org/10.1111/j.1467-8535.2006.00621.x
Wang, T. H. (2011). Developing Web-based assessment strategies for facilitating junior high school students to perform self-regulated learning in an e-Learning environment. Computers & Education, 57(2), 1801-1812. https://doi.org/10.1016/j.compedu.2011.01.003
Williams, L., Wiebe, E., Yang, K., Ferzli, M., & Miller, C. (2002). In Support of Pair Programming in the Introductory Computer Science Course. Computer Science Education, 12(3), 197-212. https://doi.org/10.1076/csed.12.3.197.8618
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society a-Mathematical Physical and Engineering Sciences, 366(1881), 3717-3725. https://doi.org/10.1098/rsta.2008.0118
Winne, P. H. (2017). Learning analytics for self-regulated learning. Handbook of learning analytics, 241-249. https://doi.org/10.18608/hla17.021
Winne, P. H., & Hadwin, A. F. (2010). nStudy: Tracing and supporting self-regulated learning in the Internet. International handbook of metacognition and learning technologies, 293-308. https://doi.org/10.1007/978-1-4419-5546-3_20
Wood, D. F. (2008). Problem based learning. BMJ, 336(7651), 971. https://doi.org/10.1136/bmj.39546.716053.80
Yadav, A., Mayfield, C., Zhou, N. E., Hambrusch, S., & Korb, J. T. (2014). Computational Thinking in Elementary and Secondary Teacher Education. ACM Transactions on Computing Education, 14(1), 1-16. https://doi.org/10.1145/2576872
Yadav, A., Stephenson, C., & Hong, H. (2017). Computational Thinking for Teacher Education. Communications of the ACM, 60(4), 55-62. https://doi.org/10.1145/2994591
Yağcı, M. (2017). Web-Mediated Problem-Based Learning and Computer Programming: Effects of Study Approach on Academic Achievement and Attitude. Journal of Educational Computing Research, 56(2), 272-292. https://doi.org/10.1177/0735633117706908
Yi-Ran, H., Cheng, Z., Feng, Y., & Meng-Xiao, Y. (2010). Research on teaching operating systems course using problem-based learning. 2010 5th International Conference on Computer Science & Education, 691-694. https://doi.org/10.1109/ICCSE.2010.5593517
Yoo, Y., Lee, H., Jo, I.-H., & Park, Y. (2015). Educational dashboards for smart learning: Review of case studies. Emerging issues in smart learning, 145-155. https://doi.org/10.1007/978-3-662-44188-6_21
Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers′ self-efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789-801. https://doi.org/10.1111/bjet.12453
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339. https://doi.org/10.1037/0022-0663.81.3.329
Zimmerman, B. J. (2000). Attaining Self-Regulation. In Handbook of Self-Regulation (pp. 13-39). Elsevier. https://doi.org/10.1016/b978-012109890-2/50031-7
Zimmerman, B. J., & Risemberg, R. (1997). Becoming a self-regulated writer: A social cognitive perspective. Contemporary Educational Psychology, 22(1), 73-101. https://doi.org/10.1006/ceps.1997.0919
Zimmerman, B. J., & Schunk, D. H. (2011). Self-regulated learning and performance: An introduction and an overview. Handbook of self-regulation of learning and performance, 15-26. https://doi.org/10.4324/9780203839010
Zimmerman, B. J., & Schunk, D. H. (2013). Self-Regulated Learning and Academic Achievement. Routledge. https://doi.org/10.4324/9781410601032 |