本論文探討自我評量在自我調節科學學習框架中的作用，強調學習者自我評量的能力對於提高學習成果的重要性，並將自我評量作為實現科學自我調節學習的關鍵機制。本論文採漏斗方法，由相互關聯的研究開展，每項研究對於整合自我評估策略對自我調節學習的影響提供了獨特的視角。
第一個研究以主題分析探索教師將自我調節學習策略納入科學教育的看法，並且透過路徑分析建立教師自我調整學習與科技教學內容知識的結構模型，凸顯對於科技輔助自我調節學習價值的共識。第二項研究進一步的探討科技輔助學習環境對學習社會科學議題論證的學生自我調節學習的影響，證明自我評量工具在自我調節成果上的關鍵預測作用。第三項研究評估自我評量設計對學生在社會科學議題論證中的學習和自我調節的影響，發現調適任務在促進認知和後設認知發展上的重要性。第四項研究提供一個全面的文獻綜述，橫跨十年（2013-2023年），以綜合洞察自我評量方法如何增強學生的自我調節科學學習，識別出在促進高層次思考技能方面應用這些策略的研究缺口。
綜合這些發現，倡議將自我評量作為一種策略性的學習方法，特別是結合得分預測、以標準進行自我評量和調適任務，以促進科學教育中的自我調節學習。本論文發現其在增強認知和後設認知方面的實際可行性和有效性。此外，本論文也強調在科技輔助學習環境中納入明確的監控和調節策略的必要性，以支持自我調節學習過程。

This dissertation explores the role of self-assessment within the framework of self-regulated science learning, emphasizing the importance of learners' self-assessment abilities in enhancing learning outcomes. It positions self-assessment as a crucial mechanism for achieving self-regulated science learning. The dissertation employs a funnel approach, conducting interrelated studies, with each study offering a unique perspective on integrating self-assessment strategies into self-regulated learning.
The first study uses thematic analysis to explore teachers' perceptions of incorporating self-regulated learning strategies into science education. Through path analysis, it establishes a structural model linking teachers' self-regulated learning with technological pedagogical content knowledge, highlighting a consensus on the value of technology-assisted self-regulated learning. The second study further investigates the impact of technology-assisted learning environments on students' self-regulated learning in arguing social science issues, demonstrating the pivotal predictive role of self-assessment tools in self-regulated outcomes. The third study assesses the impact of self-assessment design on students' learning and self-regulation in social science issue argumentation, revealing the importance of adaptive tasks in promoting cognitive and metacognitive development. The fourth study provides a comprehensive literature review spanning a decade (2013-2023), synthesizing insights on how self-assessment methods enhance students' self-regulated science learning and identifying research gaps in applying these strategies to foster higher-order thinking skills.
Integrating these findings, the dissertation advocates for self-assessment as a strategic learning approach, particularly by combining score prediction, standard-based self-assessment, and adaptive tasks to promote self-regulated learning in science education. The dissertation demonstrates the practical feasibility and effectiveness of these methods in enhancing cognitive and metacognitive aspects. Additionally, it underscores the necessity of incorporating explicit monitoring and regulation strategies in technology-assisted learning environments to support self-regulated learning processes.

American Psychological Association. (2020). Publication manual of the American Psychological Association, 7th ed[doi:10.1037/0000165-000]. American Psychological Association. https://doi.org/10.1037/0000165-000
Abbitt, J. T. (2011). Measuring Technological Pedagogical Content Knowledge in Preservice Teacher Education. Journal of Research on Technology in Education, 43(4), 281-300. https://doi.org/10.1080/15391523.2011.10782573
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/https://doi.org/10.1016/j.chb.2015.11.036
Acar, Ö., Türkmen, L., & Bilgin, A. (2015). Examination of Gender Differences on Cognitive and Motivational Factors that Influence 8th Graders’ Science Achievement in Turkey. EURASIA Journal of Mathematics, Science and Technology Education, 11(5). https://doi.org/10.12973/eurasia.2015.1372a
Acharya, A. S., Prakash, A., Saxena, P., & Nigam, A. (2013). Sampling: Why and how of it. Indian Journal of Medical Specialties, 4(2), 330-333.
Agresti, A. (2019). Categorical data analysis (Vol. 792). John Wiley & Sons. https://doi.org/10.1002/0471249688
Akpınar, E. (2014). The Use of Interactive Computer Animations Based on POE as a Presentation Tool in Primary Science Teaching. Journal of Science Education and Technology, 23(4), 527-537. https://doi.org/10.1007/s10956-013-9482-4
Al-Balushi, S. M., Al-Musawi, A. S., Ambusaidi, A. K., & Al-Hajri, F. H. (2017). The Effectiveness of Interacting with Scientific Animations in Chemistry Using Mobile Devices on Grade 12 Students’ Spatial Ability and Scientific Reasoning Skills. Journal of Science Education and Technology, 26(1), 70-81. https://doi.org/10.1007/s10956-016-9652-2
Almerich, G., Orellana, N., Suárez-Rodríguez, J., & Díaz-García, I. (2016). Teachers’ information and communication technology competences: A structural approach. Computers & Education, 100, 110-125. https://doi.org/https://doi.org/10.1016/j.compedu.2016.05.002
An, F., Xi, L., & Yu, J. (2023). The relationship between technology acceptance and self-regulated learning: the mediation roles of intrinsic motivation and learning engagement. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11959-3
Anderson, R. C., & Pearson, P. D. (1984). A schema-theoretic view of basic processes in reading comprehension. Handbook of reading research, 1, 255-291.
Anthonysamy, L., Koo, A.-C., & Hew, S.-H. (2020). Self-regulated learning strategies and non-academic outcomes in higher education blended learning environments: A one decade review. Education and Information Technologies, 25(5), 3677-3704. https://doi.org/10.1007/s10639-020-10134-2
Asparouhov, T., & Muthén, B. (2021). Advances in Bayesian Model Fit Evaluation for Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 1-14. https://doi.org/10.1080/10705511.2020.1764360
Avalos, B. (2011). Teacher professional development in Teaching and Teacher Education over ten years. Teaching and Teacher Education, 27(1), 10-20. https://doi.org/https://doi.org/10.1016/j.tate.2010.08.007
Azevedo, R., & Cromley, J. G. (2004). Does Training on Self-Regulated Learning Facilitate Students' Learning With Hypermedia? Journal of Educational Psychology, 96, 523-535. https://doi.org/10.1037/0022-0663.96.3.523
Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29(3), 344-370. https://doi.org/https://doi.org/10.1016/j.cedpsych.2003.09.002
Baars, M., Vink, S., van Gog, T., de Bruin, A., & Paas, F. (2014). Effects of training self-assessment and using assessment standards on retrospective and prospective monitoring of problem solving. Learning and Instruction, 33, 92-107. https://doi.org/10.1016/j.learninstruc.2014.04.004
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28).
Bao, L., Cai, T., Koenig, K., Fang, K., Han, J., Wang, J., . . . Wu, N. (2009). Learning and Scientific Reasoning. Science, 323(5914), 586-587. https://doi.org/doi:10.1126/science.1167740
Bao, L., & Koenig, K. (2019). Physics education research for 21st century learning. Disciplinary and Interdisciplinary Science Education Research, 1(1). https://doi.org/10.1186/s43031-019-0007-8
Bao, L., Koenig, K., Xiao, Y., Fritchman, J., Zhou, S., & Chen, C. (2022). Theoretical model and quantitative assessment of scientific thinking and reasoning. Physical Review Physics Education Research, 18(1). https://doi.org/10.1103/PhysRevPhysEducRes.18.010115
Berglas-Shapiro, T., Eylon, B.-S., & Scherz, Z. (2017). A Technology-Enhanced Intervention for Self-Regulated Learning in Science. Teachers College Record, 119(13), 1-26. https://doi.org/10.1177/016146811711901301
Berland, L. K., & McNeill, K. L. (2010). A learning progression for scientific argumentation: Understanding student work and designing supportive instructional contexts. Science Education, 94(5), 765-793. https://doi.org/https://doi.org/10.1002/sce.20402
Binbasaran Tuysuzoglu, B., & Greene, J. A. (2015). An investigation of the role of contingent metacognitive behavior in self-regulated learning. Metacognition and Learning, 10(1), 77-98. https://doi.org/10.1007/s11409-014-9126-y
Boekaerts, M. (1996). Self-regulated Learning at the Junction of Cognition and Motivation. European Psychologist, 1(2), 100-112. https://doi.org/10.1027/1016-9040.1.2.100
Boud, D. (2013). Enhancing learning through self-assessment. Routledge.
Bouffard-Bouchard, T., Parent, S., & Larivee, S. (1991). Influence of self-efficacy on self-regulation and performance among junior and senior high-school age students. International journal of behavioral development, 14(2), 153-164.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589-597. https://doi.org/10.1080/2159676X.2019.1628806
Brinson, J. R. (2015). Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education, 87, 218-237. https://doi.org/10.1016/j.compedu.2015.07.003
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1-13. https://doi.org/https://doi.org/10.1016/j.iheduc.2015.04.007
Brown, A. L. (1978). Knowing when, where, and how to remember; a problem of metacognition. Advances in instructional psychology.
Bulu, S. T., & Pedersen, S. (2010). Scaffolding middle school students’ content knowledge and ill-structured problem solving in a problem-based hypermedia learning environment. Educational Technology Research and Development, 58(5), 507-529. https://doi.org/10.1007/s11423-010-9150-9
Butler, D. L., Lauscher, H. N., Jarvis-Selinger, S., & Beckingham, B. (2004). Collaboration and self-regulation in teachers’ professional development. Teaching and Teacher Education, 20(5), 435-455. https://doi.org/https://doi.org/10.1016/j.tate.2004.04.003
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
Cengiz-Istanbullu, B., & Sakiz, G. (2022). Self-Regulated Learning Strategies Impact Fourth-Grade Students’ Positive Outcomes in Science Class. Journal of Baltic Science Education, 21(2), 192-206. https://doi.org/10.33225/jbse/22.21.192
Chahal, J., & Rani, N. (2022). Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables. Journal of Computing in Higher Education, 34(3), 844-867. https://doi.org/10.1007/s12528-022-09327-0
Chai, C. S., Ling Koh, J. H., Tsai, C.-C., & Lee Wee Tan, L. (2011). Modeling primary school pre-service teachers’ Technological Pedagogical Content Knowledge (TPACK) for meaningful learning with information and communication technology (ICT). Computers & Education, 57(1), 1184-1193. https://doi.org/https://doi.org/10.1016/j.compedu.2011.01.007
Chang, C.-Y. (2016). The marriage of interdisciplinary research: The old, the new, the borrowed, and the blue. Journal of Science and Innovation, 6(1), 29-39.
Chang, C.-Y., Chien, Y.-T., Chang, Y.-H., & Lin, C.-Y. (2012). MAGDAIRE: A model to foster pre-service teachers' ability in integrating ICT and teaching in Taiwan. Australasian Journal of Educational Technology, 28(6). https://doi.org/10.14742/ajet.806
Chen, C.-W. (2021). A Study on the Construction of Evaluation Indexes of Classroom for Promote Self-regulated Learning in Junior High School [國民中學促進自主學習課堂評估指標建構之研究] (in Chinese) (Publication Number 2021) [Doctoral dissertation, National Chengchi University]. Taipei City.
Chen, C.-W. (2022). The Inner Treasure of Cultivating Self-regulated Learning [陶養自主學習的內在寶藏] (in Chinese). In M.-w. Chang (Ed.), Proactively engage in SRL, encounter quality teaching and learning [預見善教，遇見樂學] (pp. 14-27). Education Department, New Taipei City Government.
Chen, H.-C., Gijlers, H., Sui, C.-J., & Chang, C.-Y. (2023). Asian Students’ Cultural Orientation and Computer Self-Efficacy Significantly Related to Online Inquiry-Based Learning Outcomes on the Go-Lab Platform. Journal of Science Education and Technology, 32(5), 743-758. https://doi.org/10.1007/s10956-023-10058-9
Chen, Z., & Klahr, D. (1999). All Other Things Being Equal: Acquisition and Transfer of the Control of Variables Strategy. Child Development, 70(5), 1098-1120. https://doi.org/https://doi.org/10.1111/1467-8624.00081
Cheng, P.-H., Molina, J., Lin, M.-C., Liu, H.-H., & Chang, C.-Y. (2022). A New TPACK Training Model for Tackling the Ongoing Challenges of COVID-19. Applied System Innovation, 5(2), 32. https://doi.org/10.3390/asi5020032
Cheng, S.-L., & Xie, K. (2018). The relations among teacher value beliefs, personal characteristics, and TPACK in intervention and non-intervention settings. Teaching and Teacher Education, 74, 98-113. https://doi.org/https://doi.org/10.1016/j.tate.2018.04.014
Chien, Y.-T., & Chang, C.-Y. (2012). Comparison of Different Instructional Multimedia Designs for Improving Student Science-Process Skill Learning. Journal of Science Education and Technology, 21(1), 106-113. https://doi.org/10.1007/s10956-011-9286-3
Chien, Y.-T., & Chang, C.-Y. (2015). Supporting Socio-scientific Argumentation in the Classroom Through Automatic Group Formation Based on Students’ Real-time Responses. In M. S. Khine (Ed.), Science Education in East Asia: Pedagogical Innovations and Research-informed Practices (pp. 549-563). Springer International Publishing. https://doi.org/10.1007/978-3-319-16390-1_22
Chien, Y.-T., Chang, C.-Y., Yeh, T.-K., & Chang, K.-E. (2012). Engaging pre-service science teachers to act as active designers of technology integration: A MAGDAIRE framework. Teaching and Teacher Education, 28(4), 578-588. https://doi.org/https://doi.org/10.1016/j.tate.2011.12.005
Chien, Y.-T., Chang, Y.-H., & Chang, C.-Y. (2016). Do we click in the right way? A meta-analytic review of clicker-integrated instruction. Educational Research Review, 17, 1-18. https://doi.org/https://doi.org/10.1016/j.edurev.2015.10.003
Chien, Y.-T., Lee, Y.-H., Li, T.-Y., & Chang, C.-Y. (2015). Examining the effects of displaying clicker voting results on high school students’ voting behaviors, discussion processes, and learning outcomes. EURASIA Journal of Mathematics, Science and Technology Education, 11(5), 1089-1104. https://doi.org/10.12973/eurasia.2015.1414a
Chin, C., & Osborne, J. (2010). Students' questions and discursive interaction: Their impact on argumentation during collaborative group discussions in science. Journal of Research in Science Teaching, 47(7), 883-908. https://doi.org/https://doi.org/10.1002/tea.20385
Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic inquiry in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86(2), 175-218. https://doi.org/10.1002/sce.10001
Cigdem, H. (2015). How Does Self-Regulation Affect Computer-Programming Achievement in a Blended Context? Contemporary Educational Technology, 6(1), 19-37. https://dergipark.org.tr/en/pub/cet/issue/25739/271522
Clarivate. (2022, 2022, June 9). KeyWords Plus generation, creation, and changes. Retrieved 2023, March 25 from https://support.clarivate.com/ScientificandAcademicResearch/s/article/KeyWords-Plus-generation-creation-and-changes?language=en_US
Corbalan, G., Kester, L., & van Merriënboer, J. J. G. (2008). Selecting learning tasks: Effects of adaptation and shared control on learning efficiency and task involvement. Contemporary Educational Psychology, 33(4), 733-756. https://doi.org/https://doi.org/10.1016/j.cedpsych.2008.02.003
Dang, N. V., Chiang, J. C., Brown, H. M., & McDonald, K. K. (2018). Curricular Activities that Promote Metacognitive Skills Impact Lower-Performing Students in an Introductory Biology Course. Journal of Microbiology & Biology Education, 19(1). https://doi.org/10.1128/jmbe.v19i1.1324
Daniels, H., Grineski, S. E., Collins, T. W., Morales, D. X., Morera, O., & Echegoyen, L. (2016). Factors influencing student gains from undergraduate research experiences at a Hispanic-serving institution. CBE—Life Sciences Education, 15(3), ar30. https://doi.org/10.1187/cbe.15-07-0163
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
de Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340(6130), 305-308. https://doi.org/10.1126/science.1230579
de Jong, T., Sotiriou, S., & Gillet, D. (2014). Innovations in STEM education: the Go-Lab federation of online labs. Smart Learning Environments, 1(1), 3. https://doi.org/10.1186/s40561-014-0003-6
Devolder, A., van Braak, J., & Tondeur, J. (2012). Supporting self-regulated learning in computer-based learning environments: systematic review of effects of scaffolding in the domain of science education. Journal of Computer Assisted Learning, 28(6), 557-573. https://doi.org/https://doi.org/10.1111/j.1365-2729.2011.00476.x
Diggle, P. (2002). Analysis of longitudinal data. Oxford university press.
Dignath, C., & Büttner, G. (2008). Components of fostering self-regulated learning among students. A meta-analysis on intervention studies at primary and secondary school level. Metacognition and Learning, 3(3), 231-264. https://doi.org/10.1007/s11409-008-9029-x
Dignath, C., Buettner, G., & Langfeldt, H.-P. (2008). How can primary school students learn self-regulated learning strategies most effectively?: A meta-analysis on self-regulation training programmes. Educational Research Review, 3(2), 101-129. https://doi.org/https://doi.org/10.1016/j.edurev.2008.02.003
Dinsmore, D. L., Alexander, P. A., & Loughlin, S. M. (2008). Focusing the Conceptual Lens on Metacognition, Self-regulation, and Self-regulated Learning. Educational Psychology Review, 20(4), 391-409. https://doi.org/10.1007/s10648-008-9083-6
Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84(3), 287-312. https://doi.org/10.1002/(sici)1098-237x(200005)84:3<287::Aid-sce1>3.0.Co;2-a
Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216-222. https://doi.org/https://doi.org/10.1016/0370-2693(87)91197-X
Dunlosky, J., Hartwig, M. K., Rawson, K. A., & Lipko, A. R. (2011). Improving college students' evaluation of text learning using idea-unit standards. Quarterly Journal of Experimental Psychology, 64(3), 467-484. https://doi.org/10.1080/17470218.2010.502239
Eekelen, I. M. V., Boshuizen, H. P. A., & Vermunt, J. D. (2005). Self-regulation in Higher Education Teacher Learning. Higher Education, 50(3), 447-471. https://doi.org/10.1007/s10734-004-6362-0
Efklides, A. (2011). Interactions of Metacognition With Motivation and Affect in Self-Regulated Learning: The MASRL Model. Educational Psychologist, 46(1), 6-25. https://doi.org/10.1080/00461520.2011.538645
Eisinga, R., te Grotenhuis, M., & Pelzer, B. (2013). The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? International Journal of Public Health, 58(4), 637-642. https://doi.org/10.1007/s00038-012-0416-3
Erduran, S., Simon, S., & Osborne, J. (2004). TAPping into argumentation: Developments in the application of Toulmin's Argument Pattern for studying science discourse. Science Education, 88(6), 915-933. https://doi.org/https://doi.org/10.1002/sce.20012
Ergen, B., & Kanadli, S. (2017). The effect of self-regulated learning strategies on academic achievement: A meta-analysis study. Eurasian Journal of Educational Research, 17(69), 55-74. https://dergipark.org.tr/en/pub/ejer/issue/42462/511430
Evagorou, M., Erduran, S., & Mäntylä, T. (2015). The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works. International Journal of STEM Education, 2(1), 11. https://doi.org/10.1186/s40594-015-0024-x
Falchikov, N., & Boud, D. (1989). Student Self-Assessment in Higher Education: A Meta-Analysis. Review of Educational Research, 59(4), 395-430. https://doi.org/10.3102/00346543059004395
Fang, S.-C., Hsu, Y.-S., & Lin, S.-S. (2019). Conceptualizing Socioscientific Decision Making from a Review of Research in Science Education. International Journal of Science and Mathematics Education, 17(3), 427-448. https://doi.org/10.1007/s10763-018-9890-2
Fisher, R. J. (1993). Social Desirability Bias and the Validity of Indirect Questioning. Journal of Consumer Research, 20(2), 303-315. https://doi.org/10.1086/209351
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34, 906-911. https://doi.org/10.1037/0003-066X.34.10.906
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Fukuda, M., Hajian, S., Jain, M., Liu, A. L., Obaid, T., Nesbit, J. C., & Winne, P. H. (2022). Scientific inquiry learning with a simulation: providing within-task guidance tailored to learners’ understanding and inquiry skill. International Journal of Science Education, 44(6), 1021-1043. https://doi.org/10.1080/09500693.2022.2062799
Gao, Y., Zhai, X., Andersson, B., Zeng, P., & Xin, T. (2020). Developing a Learning Progression of Buoyancy to Model Conceptual Change: A Latent Class and Rule Space Model Analysis. Research in Science Education, 50(4), 1369-1388. https://doi.org/10.1007/s11165-018-9736-5
Garcia-Mila, M., Gilabert, S., Erduran, S., & Felton, M. (2013). The Effect of Argumentative Task Goal on the Quality of Argumentative Discourse. Science Education, 97(4), 497-523. https://doi.org/https://doi.org/10.1002/sce.21057
Garnier-Villarreal, M., & Jorgensen, T. D. (2020). Adapting fit indices for Bayesian structural equation modeling: Comparison to maximum likelihood. Psychological Methods, 25, 46-70. https://doi.org/10.1037/met0000224
Gidalevich, S., & Kramarski, B. (2019). The value of fixed versus faded self-regulatory scaffolds on fourth graders’ mathematical problem solving. Instructional Science, 47(1), 39-68. https://doi.org/10.1007/s11251-018-9475-z
Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4).
Gordon, S. C., Dembo, M. H., & Hocevar, D. (2007). Do teachers’ own learning behaviors influence their classroom goal orientation and control ideology? Teaching and Teacher Education, 23(1), 36-46. https://doi.org/10.1016/j.tate.2004.08.002
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality, 37(6), 504-528. https://doi.org/10.1016/s0092-6566(03)00046-1
Greene, J. A., & Azevedo, R. (2007). A Theoretical Review of Winne and Hadwin’s Model of Self-Regulated Learning: New Perspectives and Directions. Review of Educational Research, 77(3), 334-372. https://doi.org/10.3102/003465430303953
Greiff, S., Molnár, G., Martin, R., Zimmermann, J., & Csapó, B. (2018). Students' exploration strategies in computer-simulated complex problem environments: A latent class approach. Computers & Education, 126, 248-263. https://doi.org/10.1016/j.compedu.2018.07.013
Grineski, S., Daniels, H., Collins, T., Morales, D. X., Frederick, A., & Garcia, M. (2018). The conundrum of social class: Disparities in publishing among STEM students in undergraduate research programs at a Hispanic majority institution. Science Education, 102(2), 283-303. https://doi.org/10.1002/sce.21330
Guo, K., Zhong, Y., Li, D., & Chu, S. K. W. (2023). Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation. Computers & Education, 203, 104862. https://doi.org/https://doi.org/10.1016/j.compedu.2023.104862
Guo, L., Huang, J., & Zhang, Y. (2019). Education Development in China: Education Return, Quality, and Equity. Sustainability, 11(13), 3750. https://www.mdpi.com/2071-1050/11/13/3750
Haberman, S. J. (1973). The Analysis of Residuals in Cross-Classified Tables. Biometrics, 29(1), 205-220. https://doi.org/10.2307/2529686
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
Hadwin, A. F., Järvelä, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. In Handbook of self-regulation of learning and performance. (pp. 65-84). Routledge/Taylor & Francis Group.
Harris, K. R., Lane, K. L., Graham, S., Driscoll, S. A., Sandmel, K., Brindle, M., & Schatschneider, C. (2012). Practice-Based Professional Development for Self-Regulated Strategies Development in Writing. Journal of Teacher Education, 63(2), 103-119. https://doi.org/10.1177/0022487111429005
Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. Routledge. https://doi.org/10.4324/9780203181522
Hattie, J. (2013). Calibration and confidence: Where to next? Learning and Instruction, 24, 62-66. https://doi.org/https://doi.org/10.1016/j.learninstruc.2012.05.009
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36-53. https://doi.org/https://doi.org/10.1016/j.compedu.2015.09.005
Heradio, R., de la Torre, L., Galan, D., Cabrerizo, F. J., Herrera-Viedma, E., & Dormido, S. (2016). Virtual and remote labs in education: A bibliometric analysis. Computers & Education, 98, 14-38. https://doi.org/10.1016/j.compedu.2016.03.010
Holland, D. D., & Piper, R. T. (2016). Testing a Technology Integration Education Model for Millennial Preservice Teachers:Exploring the Moderating Relationships of Goals, Feedback, Task Value, and Self-Regulation Among Motivation and Technological, Pedagogical, and Content Knowledge Competencies. Journal of Educational Computing Research, 54(2), 196-224. https://doi.org/10.1177/0735633115615129
Hong, J.-C., Lee, Y.-F., & Ye, J.-H. (2021). Procrastination predicts online self-regulated learning and online learning ineffectiveness during the coronavirus lockdown. Personality and Individual Differences, 174, 110673. https://doi.org/https://doi.org/10.1016/j.paid.2021.110673
Hsu, C.-C., Chiu, C.-H., Lin, C.-H., & Wang, T.-I. (2015). Enhancing skill in constructing scientific explanations using a structured argumentation scaffold in scientific inquiry. Computers & Education, 91, 46-59. https://doi.org/10.1016/j.compedu.2015.09.009
Hsu, Y.-S., Yen, M.-H., Chang, W.-H., Wang, C.-Y., & Chen, S. (2016). CONTENT ANALYSIS OF 1998–2012 EMPIRICAL STUDIES IN SCIENCE READING USING A SELF-REGULATED LEARNING LENS. International Journal of Science and Mathematics Education, 14(1), 1-27. https://doi.org/10.1007/s10763-014-9574-5
Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Huang, L., & Lajoie, S. P. (2021). Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development. Computers & Education, 166. https://doi.org/10.1016/j.compedu.2021.104169
Huang, L., Li, S., Poitras, E. G., & Lajoie, S. P. (2020). Latent profiles of self‐regulated learning and their impacts on teachers’ technology integration. British Journal of Educational Technology, 52(2), 695-713. https://doi.org/10.1111/bjet.13050
Ishaq, E., Bashir, S., Zakariya, R., & Sarwar, A. (2021). Technology Acceptance Behavior and Feedback Loop: Exploring Reverse Causality of TAM in Post-COVID-19 Scenario [Hypothesis and Theory]. Frontiers in Psychology, 12. https://www.frontiersin.org/articles/10.3389/fpsyg.2021.682507
Jang, S.-J., & Tsai, M.-F. (2013). Exploring the TPACK of Taiwanese secondary school science teachers using a new contextualized TPACK model. Australasian Journal of Educational Technology, 29(4). https://doi.org/10.14742/ajet.282
Jiménez-Aleixandre, M. P., & Erduran, S. (2007). Argumentation in Science Education: An Overview. In S. Erduran & M. P. Jiménez-Aleixandre (Eds.), Argumentation in Science Education: Perspectives from Classroom-Based Research (pp. 3-27). Springer Netherlands. https://doi.org/10.1007/978-1-4020-6670-2_1
Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7(2), 75-83. https://doi.org/https://doi.org/10.1111/j.1365-2729.1991.tb00230.x
Khan, S., & Krell, M. (2021). Patterns of Scientific Reasoning Skills among Pre-Service Science Teachers: A Latent Class Analysis. Education Sciences, 11(10). https://doi.org/10.3390/educsci11100647
Kim, D., Lee, Y., Leite, W. L., & Huggins-Manley, A. C. (2020). Exploring student and teacher usage patterns associated with student attrition in an open educational resource-supported online learning platform. Computers & Education, 156, 103961. https://doi.org/10.1016/j.compedu.2020.103961
Kitchener, K. S. (1983). Cognition, metacognition, and epistemic cognition. Human Development, 26(4), 222-232.
Kline, R. B. (2011). Principles and practice of structural equation modeling, 3rd ed. Guilford Press.
Knight, J. K., Weaver, D. C., Peffer, M. E., & Hazlett, Z. S. (2022). Relationships between Prediction Accuracy, Metacognitive Reflection, and Performance in Introductory Genetics Students. CBE—Life Sciences Education, 21(3), ar45. https://doi.org/10.1187/cbe.21-12-0341
Koenig, K., Wood, K. E., Bortner, L. J., & Bao, L. (2019). Modifying Traditional Labs to Target Scientific Reasoning. Journal of College Science Teaching, 48(5), 28-35. https://www.jstor.org/stable/26901313
Koh, J. H. L., Chai, C. S., & Tsai, C.-C. (2013). Examining practicing teachers’ perceptions of technological pedagogical content knowledge (TPACK) pathways: a structural equation modeling approach. Instructional Science, 41(4), 793-809. https://doi.org/10.1007/s11251-012-9249-y
Koh, J. H. L., Chai, C. S., & Tsai, C. C. (2010). Examining the technological pedagogical content knowledge of Singapore pre-service teachers with a large-scale survey. Journal of Computer Assisted Learning, 26(6), 563-573. https://doi.org/10.1111/j.1365-2729.2010.00372.x
Kostons, D., van Gog, T., & Paas, F. (2010). Self-assessment and task selection in learner-controlled instruction: Differences between effective and ineffective learners. Computers & Education, 54(4), 932-940. https://doi.org/https://doi.org/10.1016/j.compedu.2009.09.025
Kostons, D., van Gog, T., & Paas, F. (2012). Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning. Learning and Instruction, 22(2), 121-132. https://doi.org/https://doi.org/10.1016/j.learninstruc.2011.08.004
Kramarski, B., & Michalsky, T. (2010). Preparing preservice teachers for self-regulated learning in the context of technological pedagogical content knowledge. Learning and Instruction, 20(5), 434-447. https://doi.org/10.1016/j.learninstruc.2009.05.003
Krebs, R., Rothstein, B., & Roelle, J. (2022). Rubrics enhance accuracy and reduce cognitive load in self-assessment. Metacognition and Learning, 17(2), 627-650. https://doi.org/10.1007/s11409-022-09302-1
Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2014). Students’ Levels of Understanding Models and Modelling in Biology: Global or Aspect-Dependent? Research in Science Education, 44(1), 109-132. https://doi.org/10.1007/s11165-013-9365-y
Kruschke, J. K., & Liddell, T. M. (2018). Bayesian data analysis for newcomers. Psychonomic Bulletin & Review, 25(1), 155-177. https://doi.org/10.3758/s13423-017-1272-1
Kuhn, D. (1991). The skills of argument. Cambridge University Press.
Kuhn, D. (2007). Reasoning about multiple variables: Control of variables is not the only challenge. Science Education, 91(5), 710-726. https://doi.org/10.1002/sce.20214
Kuhn, D., & Dean Jr, D. (2004). Connecting Scientific Reasoning and Causal Inference. Journal of Cognition and Development, 5(2), 261-288. https://doi.org/10.1207/s15327647jcd0502_5
Kuhn, D., & Pease, M. (2008). What Needs to Develop in the Development of Inquiry Skills? Cognition and Instruction, 26(4), 512-559. https://doi.org/10.1080/07370000802391745
Kulgemeyer, C. (2018). Impact of Secondary Students’ Content Knowledge on Their Communication Skills in Science. International Journal of Science and Mathematics Education, 16(1), 89-108. https://doi.org/10.1007/s10763-016-9762-6
Lai, C.-L., Hwang, G.-J., & Tu, Y.-H. (2018). The effects of computer-supported self-regulation in science inquiry on learning outcomes, learning processes, and self-efficacy. Educational Technology Research and Development, 66(4), 863-892. https://doi.org/10.1007/s11423-018-9585-y
Lai, C. (2013). A Framework for Developing Self-Directed Technology Use for Language Learning. Language Learning & Technology, 17(2), 100–122. https://doi.org/10125/44326
Landis, J. R., & Koch, G. G. (1977). An Application of Hierarchical Kappa-type Statistics in the Assessment of Majority Agreement among Multiple Observers. Biometrics, 33(2), 363-374. https://doi.org/10.2307/2529786
Lau, K. L., & Jong, M. S. Y. (2022). Acceptance of and self-regulatory practices in online learning and their effects on the participation of Hong Kong secondary school students in online learning. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11546-y
Lawson, A. E., Clark, B., Cramer-Meldrum, E., Falconer, K. A., Sequist, J. M., & Kwon, Y.-J. (2000). Development of Scientific Reasoning in College Biology: Do Two Levels of General Hypothesis-Testing Skills Exist? Journal of Research in Science Teaching, 37(1), 81-101. https://doi.org/10.1002/(SICI)1098-2736(200001)37:1<81::AID-TEA6>3.0.CO;2-I
León, J., Núñez, J. L., & Liew, J. (2015). Self-determination and STEM education: Effects of autonomy, motivation, and self-regulated learning on high school math achievement. Learning and Individual Differences, 43, 156-163. https://doi.org/https://doi.org/10.1016/j.lindif.2015.08.017
León, S. P., Panadero, E., & García-Martínez, I. (2023). How Accurate Are Our Students? A Meta-analytic Systematic Review on Self-assessment Scoring Accuracy. Educational Psychology Review, 35(4), 106. https://doi.org/10.1007/s10648-023-09819-0
Lead States NGSS. (2013). Next Generation Science Standards: For States, By States. The National Academies Press. https://doi.org/doi:10.17226/18290
Lee, C.-J., & Kim, C. (2014). An implementation study of a TPACK-based instructional design model in a technology integration course. Educational Technology Research and Development, 62(4), 437-460. https://doi.org/10.1007/s11423-014-9335-8
Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208. https://doi.org/https://doi.org/10.1016/j.compedu.2012.10.001
Lee, M. H., Wu, Y. T., & Tsai, C. C. (2009). Research Trends in Science Education from 2003 to 2007: A content analysis of publications in selected journals. International Journal of Science Education, 31(15), 1999-2020. https://doi.org/10.1080/09500690802314876
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204. https://doi.org/https://doi.org/10.1016/S0378-7206(01)00143-4
Li, W.-C., Nichanian, A., Lin, J., & Braithwaite, G. (2022). Investigating the impacts of COVID-19 on aviation safety based on occurrences captured through flight data monitoring. Ergonomics, 1-15. https://doi.org/10.1080/00140139.2022.2155317
Li, X., Wang, L., Shen, J., Wang, J., Hu, W., Chen, Y., & Tian, R. (2018). Analysis and comparison of scientific inquiry activities in eight-grade physics textbooks in China. Journal of Baltic Science Education, 17(2), 229. https://doi.org/10.33225/jbse/18.17.229
Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13-22.
Liang, X. (2020). Prior sensitivity in Bayesian structural equation modeling for sparse factor loading structures. Educational and psychological measurement, 80(6), 1025-1058. https://doi.org/10.1177/0013164420906449
Lim, C. P., & Chan, B. C. (2007). microLESSONS in teacher education: Examining pre-service teachers’ pedagogical beliefs. Computers & Education, 48(3), 474-494. https://doi.org/https://doi.org/10.1016/j.compedu.2005.03.005
Lin, T.-C., Lin, T.-J., & Tsai, C.-C. (2014). Research Trends in Science Education from 2008 to 2012: A systematic content analysis of publications in selected journals. International Journal of Science Education, 36(8), 1346-1372. https://doi.org/10.1080/09500693.2013.864428
Lin, T.-C., Tsai, C.-C., Chai, C. S., & Lee, M.-H. (2013). Identifying Science Teachers’ Perceptions of Technological Pedagogical and Content Knowledge (TPACK). Journal of Science Education and Technology, 22(3), 325-336. https://doi.org/10.1007/s10956-012-9396-6
Lin, T.-J., Lin, T.-C., Potvin, P., & Tsai, C.-C. (2019). Research trends in science education from 2013 to 2017: a systematic content analysis of publications in selected journals. International Journal of Science Education, 41(3), 367-387. https://doi.org/10.1080/09500693.2018.1550274
Lindsey, B. A., & Nagel, M. L. (2015). Do students know what they know? Exploring the accuracy of students’ self-assessments. Physical Review Special Topics - Physics Education Research, 11(2). https://doi.org/10.1103/PhysRevSTPER.11.020103
Liou, W.-K., Bhagat, K. K., & Chang, C.-Y. (2016). Beyond the Flipped Classroom: A Highly Interactive Cloud-Classroom (HIC) Embedded into Basic Materials Science Courses. Journal of Science Education and Technology, 25(3), 460-473. https://doi.org/10.1007/s10956-016-9606-8
Lipko, A. R., Dunlosky, J., Hartwig, M. K., Rawson, K. A., Swan, K., & Cook, D. (2009). Using standards to improve middle school students' accuracy at evaluating the quality of their recall. Journal of Experimental Psychology: Applied, 15(4), 307-318. https://doi.org/10.1037/a0017599
Liu, X., Liang, L. L., & Liu, E. (2012). Science Education Research in China: Challenges and Promises. International Journal of Science Education, 34(13), 1961-1970. https://doi.org/10.1080/09500693.2012.709330
Ly, A., Marsman, M., & Wagenmakers, E.-J. (2018). Analytic posteriors for Pearson's correlation coefficient. Statistica Neerlandica, 72(1), 4-13. https://doi.org/https://doi.org/10.1111/stan.12111
Ly, A., Verhagen, J., & Wagenmakers, E.-J. (2016). Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology. Journal of Mathematical Psychology, 72, 19-32. https://doi.org/https://doi.org/10.1016/j.jmp.2015.06.004
Manning, B. H., & Payne, B. D. (1993). A Vygotskian-based theory of teacher cognition: Toward the acquisition of mental reflection and self-regulation. Teaching and Teacher Education, 9(4), 361-371. https://doi.org/https://doi.org/10.1016/0742-051X(93)90003-Y
Mathabathe, K. C., & Potgieter, M. (2014). Metacognitive monitoring and learning gain in foundation chemistry. Chemistry Education Research and Practice, 15(1), 94-104. https://doi.org/10.1039/c3rp00119a
Mayer, P., & Girwidz, R. (2019). Physics Teachers' Acceptance of Multimedia Applications—Adaptation of the Technology Acceptance Model to Investigate the Influence of TPACK on Physics Teachers' Acceptance Behavior of Multimedia Applications [Original Research]. Frontiers in Education, 4. https://doi.org/10.3389/feduc.2019.00073
Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions? Educational Psychologist, 32(1), 1-19.
Mayer, R. E., & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84(4), 444. https://doi.org/10.1037/0022-0663.84.4.444
McDowell, L. D. (2019). The roles of motivation and metacognition in producing self-regulated learners of college physical science: a review of empirical studies. International Journal of Science Education, 41(17), 2524-2541. https://doi.org/10.1080/09500693.2019.1689584
McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC. https://doi.org/10.1201/9780429029608
McKevitt, C. T. (2016). Engaging students with self-assessment and tutor feedback to improve performance and support assessment capacity. Journal of University Teaching & Learning Practice, 13(1), 2. https://doi.org/10.53761/1.13.1.2
McNeill, K. L. (2009). Teachers' use of curriculum to support students in writing scientific arguments to explain phenomena. Science Education, 93(2), 233-268. https://doi.org/https://doi.org/10.1002/sce.20294
McNeill, K. L. (2011). Elementary students' views of explanation, argumentation, and evidence, and their abilities to construct arguments over the school year. Journal of Research in Science Teaching, 48(7), 793-823. https://doi.org/10.1002/tea.20430
Merkle, E. C., & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1-30. https://doi.org/10.18637/jss.v085.i04
Ministry of Education. (2001). National science curriculum standards of the compulsory education (Gr. 7–9). In. Beijing: People’s Education Press (in Chinese).
Ministry of Education. (2003a). National physics curriculum standards for high school. In. Beijing: People’s Education Press (in Chinese).
Ministry of Education. (2003b). National chemistry curriculum standards for high school. In. Beijing: People’s Education Press (in Chinese).
Ministry of Education. (2003c). National biology curriculum standards for high school. In. Beijing: People’s Education Press (in Chinese).
Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record, 108(6), 1017-1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, a. t. P. (2009). Reprint—Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Physical Therapy, 89(9), 873-880. https://doi.org/10.1093/ptj/89.9.873
Moos, D. C., & Ringdal, A. (2012). Self-Regulated Learning in the Classroom: A Literature Review on the Teacher’s Role. Education Research International, 2012, 423284. https://doi.org/10.1155/2012/423284
Morales, D. X., Grineski, S. E., & Collins, T. W. (2018). Effects of gender concordance in mentoring relationships on summer research experience outcomes for undergraduate students. Science Education, 102(5), 1029-1050. https://doi.org/10.1002/sce.21455
Morphew, J. W. (2020). Changes in metacognitive monitoring accuracy in an introductory physics course. Metacognition and Learning, 16(1), 89-111. https://doi.org/10.1007/s11409-020-09239-3
Munyengabe, S., Yiyi, Z., Haiyan, H., & Hitimana, S. (2017). Primary teachers’ perceptions on ICT integration for enhancing teaching and learning through the implementation of one laptop per child program in primary schools of Rwanda. EURASIA Journal of Mathematics, Science and Technology Education, 13(11), 7193-7204. https://doi.org/10.12973/ejmste/79044
Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313-335. https://doi.org/10.1037/a0026802
National Bureau of Statistics. (2021). The Seventh National Population Census Bulletin. Retrieved 2023, May 5 from http://big5.www.gov.cn/gate/big5/www.gov.cn/guoqing/2021-05/13/content_5606149.htm
National Research Council. (1996). National Science Education Standards. The National Academies Press. https://doi.org/doi:10.17226/4962
National Research Council. (2000). Inquiry and the National Science Education Standards: A Guide for Teaching and Learning. The National Academies Press. https://doi.org/doi:10.17226/9596
National Survey Research Center. (2013). China Education Panel Survey. In. Beijing: National Survey Research Center.
Nederhand, M. L., Tabbers, H. K., Jongerling, J., & Rikers, R. M. J. P. (2020). Reflection on exam grades to improve calibration of secondary school students: a longitudinal study. Metacognition and Learning, 15(3), 291-317. https://doi.org/10.1007/s11409-020-09233-9
Nelson, T. O. (1990). Metamemory: A Theoretical Framework and New Findings. In G. H. Bower (Ed.), Psychology of Learning and Motivation (Vol. 26, pp. 125-173). Academic Press. https://doi.org/https://doi.org/10.1016/S0079-7421(08)60053-5
Nelson, T. O., & Dunlosky, J. (1991). When People's Judgments of Learning (JOLs) are Extremely Accurate at Predicting Subsequent Recall: The “Delayed-JOL Effect”. Psychological Science, 2(4), 267-271. https://doi.org/10.1111/j.1467-9280.1991.tb00147.x
Nelson, T. O., & Narens, L. (1994). Why investigate metacognition. Metacognition: Knowing about knowing, 13, 1-25.
NGSS Lead States. (2013). Next generation science standards: For states, by states. In. Washington, DC: The National Academies Press.
Nieminen, P., Savinainen, A., & Viiri, J. (2013). Gender Differences in Learning of the Concept of Force, Representational Consistency, and Scientific Reasoning. International Journal of Science and Mathematics Education, 11(5), 1137-1156. https://doi.org/10.1007/s10763-012-9363-y
Nugteren, M. L., Jarodzka, H., Kester, L., & Van Merriënboer, J. J. G. (2018). Self-regulation of secondary school students: self-assessments are inaccurate and insufficiently used for learning-task selection. Instructional Science, 46(3), 357-381. https://doi.org/10.1007/s11251-018-9448-2
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535-569. https://doi.org/10.1080/10705510701575396
Omarchevska, Y., Lachner, A., Richter, J., & Scheiter, K. (2022). Do Video Modeling and Metacognitive Prompts Improve Self-Regulated Scientific Inquiry? Educational Psychology Review, 34(2), 1025-1061. https://doi.org/10.1007/s10648-021-09652-3
Organization for Economic Co-operation and Development. (2016). PISA 2015 Results (Volume I). https://doi.org/doi:https://doi.org/10.1787/9789264266490-en
Organization for Economic Co-operation and Development. (2017). PISA 2015 Assessment and Analytical Framework. https://doi.org/doi:https://doi.org/10.1787/9789264281820-en
Organization for Economic Co-operation and Development. (2018). The future of education and skills-Education, 2030. http://www.oecd.org/education/2030/E2030%20Position%20Paper%20(05.04.2018).pdf
Osborne, J. (2010). Arguing to Learn in Science: The Role of Collaborative, Critical Discourse. Science, 328(5977), 463-466. https://doi.org/doi:10.1126/science.1183944
Osborne, J. (2014). Teaching Scientific Practices: Meeting the Challenge of Change. Journal of Science Teacher Education, 25(2), 177-196. https://doi.org/10.1007/s10972-014-9384-1
Osborne, J., Erduran, S., & Simon, S. (2004). Enhancing the quality of argumentation in school science. Journal of Research in Science Teaching, 41(10), 994-1020. https://doi.org/10.1002/tea.20035
Osterhage, J. L., Usher, E. L., Douin, T. A., & Bailey, W. M. (2019). Opportunities for Self-Evaluation Increase Student Calibration in an Introductory Biology Course. CBE—Life Sciences Education, 18(2), ar16. https://doi.org/10.1187/cbe.18-10-0202
Paas, F. G. W. C. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84, 429-434. https://doi.org/10.1037/0022-0663.84.4.429
Pamuk, S., Ergun, M., Cakir, R., Yilmaz, H. B., & Ayas, C. (2015). Exploring relationships among TPACK components and development of the TPACK instrument. Education and Information Technologies, 20(2), 241-263. https://doi.org/10.1007/s10639-013-9278-4
Panadero, E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422
Panadero, E., & Alqassab, M. (2019). An empirical review of anonymity effects in peer assessment, peer feedback, peer review, peer evaluation and peer grading. Assessment & Evaluation in Higher Education, 44(8), 1253-1278. https://doi.org/10.1080/02602938.2019.1600186
Panadero, E., Brown, G. T. L., & Strijbos, J.-W. (2016). The Future of Student Self-Assessment: a Review of Known Unknowns and Potential Directions. Educational Psychology Review, 28(4), 803-830. https://doi.org/10.1007/s10648-015-9350-2
Panadero, E., & Jonsson, A. (2013). The use of scoring rubrics for formative assessment purposes revisited: A review. Educational Research Review, 9, 129-144. https://doi.org/https://doi.org/10.1016/j.edurev.2013.01.002
Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review, 22, 74-98. https://doi.org/10.1016/j.edurev.2017.08.004
Panadero, E., Jonsson, A., Pinedo, L., & Fernández-Castilla, B. (2023). Effects of Rubrics on Academic Performance, Self-Regulated Learning, and self-Efficacy: a Meta-analytic Review. Educational Psychology Review, 35(4), 113. https://doi.org/10.1007/s10648-023-09823-4
Panadero, E., Lipnevich, A., & Broadbent, J. (2019). Turning Self-Assessment into Self-Feedback. In M. Henderson, R. Ajjawi, D. Boud, & E. Molloy (Eds.), The Impact of Feedback in Higher Education: Improving Assessment Outcomes for Learners (pp. 147-163). Springer International Publishing. https://doi.org/10.1007/978-3-030-25112-3_9
Panadero, E., Tapia, J. A., & Huertas, J. A. (2012). Rubrics and self-assessment scripts effects on self-regulation, learning and self-efficacy in secondary education. Learning and Individual Differences, 22(6), 806-813. https://doi.org/https://doi.org/10.1016/j.lindif.2012.04.007
Pantiwati, Y., & Husamah, H. (2017). Self and Peer Assessments in Active Learning Model to Increase Metacognitive Awareness and Cognitive Abilities. International Journal of Instruction, 10(4), 185-202. https://doi.org/10.12973/iji.2017.10411a
Pazicni, S., & Bauer, C. F. (2014). Characterizing illusions of competence in introductory chemistry students. Chemistry Education Research and Practice, 15(1), 24-34. https://doi.org/10.1039/c3rp00106g
Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., . . . Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47-61. https://doi.org/https://doi.org/10.1016/j.edurev.2015.02.003
Peeters, J., De Backer, F., Reina, V. R., Kindekens, A., Buffel, T., & Lombaerts, K. (2014). The Role of Teachers’ Self-regulatory Capacities in the Implementation of Self-regulated Learning Practices. Procedia - Social and Behavioral Sciences, 116, 1963-1970. https://doi.org/https://doi.org/10.1016/j.sbspro.2014.01.504
Perry, N. E., & Winne, P. H. (2006). Learning from Learning Kits: gStudy Traces of Students’ Self-Regulated Engagements with Computerized Content. Educational Psychology Review, 18(3), 211-228. https://doi.org/10.1007/s10648-006-9014-3
Petillion, R. J., & McNeil, W. S. (2020). Johnstone’s Triangle as a Pedagogical Framework for Flipped-Class Instructional Videos in Introductory Chemistry. Journal of Chemical Education, 97(6), 1536-1542. https://doi.org/10.1021/acs.jchemed.9b01105
Pieschl, S., Bromme, R., Porsch, T., & Stahl, E. (2009). Does adaptation to task complexity guarantee success in learning? processes and conditions of beneficial adaptations in self-regulated learning. Proceedings of the31st Annual Conference of the Cognitive Science Society,
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459-470. https://doi.org/https://doi.org/10.1016/S0883-0355(99)00015-4
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and psychological measurement, 53(3), 801-813.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891. https://doi.org/10.3758/BRM.40.3.879
Raaijmakers, S. F., Baars, M., Paas, F., van Merriënboer, J. J. G., & van Gog, T. (2019). Effects of self-assessment feedback on self-assessment and task-selection accuracy. Metacognition and Learning, 14(1), 21-42. https://doi.org/10.1007/s11409-019-09189-5
Raaijmakers, S. F., Baars, M., Schaap, L., Paas, F., van Merriënboer, J., & van Gog, T. (2017). Training self-regulated learning skills with video modeling examples: Do task-selection skills transfer? Instructional Science, 46(2), 273-290. https://doi.org/10.1007/s11251-017-9434-0
Rajasekaran, R., Sreedevi, P. S., & Chang, C.-Y. (2024). DECODE-based STEM workshop in improving academic resilience and teaching competency of pre-service teachers. EURASIA Journal of Mathematics, Science and Technology Education, 20(2). https://doi.org/10.29333/ejmste/14243
Raković, M., Bernacki, M. L., Greene, J. A., Plumley, R. D., Hogan, K. A., Gates, K. M., & Panter, A. T. (2022). Examining the critical role of evaluation and adaptation in self-regulated learning. Contemporary Educational Psychology, 68. https://doi.org/10.1016/j.cedpsych.2021.102027
Reyes, V. C., Reading, C., Doyle, H., & Gregory, S. (2017). Integrating ICT into teacher education programs from a TPACK perspective: Exploring perceptions of university lecturers. Computers & Education, 115, 1-19. https://doi.org/https://doi.org/10.1016/j.compedu.2017.07.009
Rohrkemper, M. M. (1989). Self-Regulated Learning and Academic Achievement: A Vygotskian View. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-Regulated Learning and Academic Achievement: Theory, Research, and Practice (pp. 143-167). Springer New York. https://doi.org/10.1007/978-1-4612-3618-4_6
Rosenberg, J. M., Beymer, P. N., Anderson, D. J., Van Lissa, C., & Schmidt, J. A. (2019). tidyLPA: An R package to easily carry out latent profile analysis (LPA) using open-source or commercial software. Journal of Open Source Software, 3(30), 978. https://doi.org/10.21105/joss.00978
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
Ryoo, K., & Linn, M. C. (2012). Can dynamic visualizations improve middle school students' understanding of energy in photosynthesis? [https://doi.org/10.1002/tea.21003]. Journal of Research in Science Teaching, 49(2), 218-243. https://doi.org/https://doi.org/10.1002/tea.21003
Sadler, T. D. (2004). Informal reasoning regarding socioscientific issues: A critical review of research. Journal of Research in Science Teaching, 41, 513-536. https://doi.org/10.1002/tea.20009
Sadler, T. D., Barab, S. A., & Scott, B. (2007). What Do Students Gain by Engaging in Socioscientific Inquiry? Research in Science Education, 37(4), 371-391. https://doi.org/10.1007/s11165-006-9030-9
Safadi, R., & Saadi, S. (2019). Learning from Self-Diagnosis Activities when Contrasting Students’ Own Solutions with Worked Examples: the Case of 10th Graders Studying Geometric Optics. Research in Science Education, 51(2), 523-546. https://doi.org/10.1007/s11165-018-9806-8
Sanders, S., Ennis, R. P., & Losinski, M. (2018). Effects of TWA on Science Text Comprehension of Students with Emotional and Behavior Disorders in a Special Day School. Education and Treatment of Children, 41(4), 483-506. https://www.jstor.org/stable/26535288
Schlatter, E., Molenaar, I., & Lazonder, A. W. (2021). Learning scientific reasoning: A latent transition analysis. Learning and Individual Differences, 92, 102043. https://doi.org/10.1016/j.lindif.2021.102043
Schraw, G. (2008). A conceptual analysis of five measures of metacognitive monitoring. Metacognition and Learning, 4(1), 33-45. https://doi.org/10.1007/s11409-008-9031-3
Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting Self-Regulation in Science Education: Metacognition as Part of a Broader Perspective on Learning. Research in Science Education, 36(1), 111-139. https://doi.org/10.1007/s11165-005-3917-8
Schraw, G., Kauffman, D. F., & Lehman, S. (2006). Self-Regulated Learning. In Encyclopedia of Cognitive Science. https://doi.org/https://doi.org/10.1002/0470018860.s00671
Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7, 351-371. https://doi.org/10.1007/BF02212307
Schunk, D. H. (1994). Self-regulation of self-efficacy and attributions in academic settings. Self-regulation of learning and performance: Issues and educational applications, 1994, 75-99.
Shulman, L. S. (1986). Those Who Understand: Knowledge Growth in Teaching. Educational researcher, 15(2), 4-14. https://doi.org/10.3102/0013189x015002004
Singapore Ministry of Education. (2022). 21st Century Competencies. Singapore. Retrieved 12/14 from https://www.moe.gov.sg/education-in-sg/21st-century-competencies
Smid, S. C., McNeish, D., Miočević, M., & van de Schoot, R. (2020). Bayesian Versus Frequentist Estimation for Structural Equation Models in Small Sample Contexts: A Systematic Review. Structural Equation Modeling: A Multidisciplinary Journal, 27(1), 131-161. https://doi.org/10.1080/10705511.2019.1577140
Song, D., & Kim, D. (2021). Effects of self-regulation scaffolding on online participation and learning outcomes. Journal of Research on Technology in Education, 53(3), 249-263. https://doi.org/10.1080/15391523.2020.1767525
Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. https://doi.org/https://doi.org/10.1016/j.jvb.2020.103445
Stanton, J. D., Dye, K. M., & Johnson, M. (2019). Knowledge of Learning Makes a Difference: A Comparison of Metacognition in Introductory and Senior-Level Biology Students. CBE—Life Sciences Education, 18(2), ar24. https://doi.org/10.1187/cbe.18-12-0239
Stanton, J. D., Neider, X. N., Gallegos, I. J., & Clark, N. C. (2015). Differences in metacognitive regulation in introductory biology students: when prompts are not enough. CBE—Life Sciences Education, 14(2). https://doi.org/10.1187/cbe.14-08-0135
Steinbach, J., & Stoeger, H. (2016). How primary school teachers' attitudes towards self-regulated learning (SRL) influence instructional behavior and training implementation in classrooms. Teaching and Teacher Education, 60, 256-269. https://doi.org/https://doi.org/10.1016/j.tate.2016.08.017
Stephenson, C., & Isaacs, T. (2019). The role of the Extended Project Qualification in developing self-regulated learners: exploring students’ and teachers’ experiences. The Curriculum Journal, 30(4), 392-421. https://doi.org/https://doi.org/10.1080/09585176.2019.1646665
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/https://doi.org/10.1016/j.edurev.2017.02.002
Sui, C.-J., Chen, H.-C., Cheng, P.-H., & Chang, C.-Y. (2023). The Go-Lab Platform, an Inquiry-learning Space: Investigation into Students’ Technology Acceptance, Knowledge Integration, and Learning Outcomes. Journal of Science Education and Technology, 32(1), 61-77. https://doi.org/10.1007/s10956-022-10008-x
Sui, C.-J., Yen, M.-H., & Chang, C.-Y. (2024). Investigating effects of perceived technology-enhanced environment on self-regulated learning. Education and Information Technologies, 29, 161-183. https://doi.org/10.1007/s10639-023-12270-x
Taber, K. S. (2013). Revisiting the chemistry triplet: drawing upon the nature of chemical knowledge and the psychology of learning to inform chemistry education. Chemistry Education Research and Practice, 14(2), 156-168. https://doi.org/10.1039/C3RP00012E
Taiwan Minister of Education. (2014). Curriculum Guidelines of 12-Year Basic Education_General Guidelines. Minister of Education.
Tantrarungroj, P., & Suwannatthachote, P. (2013). Enhancing pre-service teacher’s self-efficacy and technological pedagogical content knowledge in designing digital media with self-regulated learning instructional support in online project-based learning. Creative Education, 3(08), 77. https://doi.org/10.4236/ce.2012.38B017
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312. https://doi.org/https://doi.org/10.1016/j.compedu.2008.08.006
Toulmin, S. E. (1958). The Uses of Argument. Philosophy, 34(130), 244-245.
Tsai, C. C., & Wen, L. M. (2005). Research and trends in science education from 1998 to 2002: a content analysis of publication in selected journals. International Journal of Science Education, 27(1), 3-14. https://doi.org/10.1080/0950069042000243727
Turner, A. J., Fung, I., Naik, V., Horowitz, L. W., & Cohen, R. C. (2018). Modulation of hydroxyl variability by ENSO in the absence of external forcing. Proceedings of the National Academy of Sciences, 115(36), 8931-8936. https://doi.org/doi:10.1073/pnas.1807532115
Tytler, R. (2001). Dimensions of evidence, the public understanding of science and science education. International Journal of Science Education, 23(8), 815-832. https://doi.org/10.1080/09500690010016058
Ulus, B., & Oner, D. (2020). Fostering Middle School Students’ Knowledge Integration Using the Web-Based Inquiry Science Environment (WISE). Journal of Science Education and Technology, 29(2), 242-256. https://doi.org/10.1007/s10956-019-09809-4
UN General Assembly. (2015). Transforming our world: the 2030 agenda for sustainable development, 21 October 2015.
Uzuntiryaki-Kondakci, E., Demirdöğen, B., Akın, F. N., Tarkin, A., & Aydın-Günbatar, S. (2017). Exploring the complexity of teaching: the interaction between teacher self-regulation and pedagogical content knowledge. Chemistry Education Research and Practice, 18(1), 250-270. https://doi.org/10.1039/c6rp00223d
van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in Teacher–Student Interaction: A Decade of Research. Educational Psychology Review, 22(3), 271-296. https://doi.org/10.1007/s10648-010-9127-6
van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., . . . Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1. https://doi.org/10.1038/s43586-020-00001-2
van de Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S. (2017). A systematic review of Bayesian articles in psychology: The last 25 years. Psychological Methods, 22, 217-239. https://doi.org/10.1037/met0000100
van der Meij, J., & de Jong, T. (2006). Supporting students' learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16(3), 199-212. https://doi.org/10.1016/j.learninstruc.2006.03.007
Veenman, M. V. (2016). Learning to self-monitor and self-regulate. In Handbook of research on learning and instruction (pp. 249-273). Routledge.
Velázquez-Marcano, A., Williamson, V. M., Ashkenazi, G., Tasker, R., & Williamson, K. C. (2004). The Use of Video Demonstrations and Particulate Animation in General Chemistry. Journal of Science Education and Technology, 13(3), 315-323. https://doi.org/10.1023/B:JOST.0000045458.76285.fe
Vemu, S., Denaro, K., Sato, B. K., & Williams, A. E. (2022). Moving the Needle: Evidence of an Effective Study Strategy Intervention in a Community College Biology Course. CBE—Life Sciences Education, 21(2), ar24. https://doi.org/10.1187/cbe.21-08-0216
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Vidergor, H. E., & Ben-Amram, P. (2020). Khan academy effectiveness: The case of math secondary students' perceptions. Computers & Education, 157, 103985. https://doi.org/https://doi.org/10.1016/j.compedu.2020.103985
Wahono, B., Hariyadi, S., & Subiantoro, A. W. (2022). The development of an online STEM teacher professional development package with the DECODE model: An innovative teacher’s quality maintenance. EURASIA Journal of Mathematics, Science and Technology Education, 18(12). https://doi.org/10.29333/ejmste/12647
Wahono, B., Lin, P.-L., & Chang, C.-Y. (2020). Evidence of STEM enactment effectiveness in Asian student learning outcomes. International Journal of STEM Education, 7(1), 36. https://doi.org/10.1186/s40594-020-00236-1
Wang, C.-Y. (2014). Scaffolding Middle School Students’ Construction of Scientific Explanations: Comparing a cognitive versus a metacognitive evaluation approach. International Journal of Science Education, 37(2), 237-271. https://doi.org/10.1080/09500693.2014.979378
Weijters, B., Cabooter, E., & Schillewaert, N. (2010). The effect of rating scale format on response styles: The number of response categories and response category labels. International Journal of Research in Marketing, 27(3), 236-247. https://doi.org/10.1016/j.ijresmar.2010.02.004
Wieman, C. E., Adams, W. K., & Perkins, K. K. (2008). PhET: Simulations That Enhance Learning. Science, 322(5902), 682-683. https://doi.org/doi:10.1126/science.1161948
Winne, P. H. (1996). A metacognitive view of individual differences in self-regulated learning. Learning and Individual Differences, 8(4), 327-353. https://doi.org/https://doi.org/10.1016/S1041-6080(96)90022-9
Winne, P. H., & Azevedo, R. (2022). Metacognition and Self-Regulated Learning. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (3 ed., pp. 93-113). Cambridge University Press. https://doi.org/DOI: 10.1017/9781108888295.007
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 279-306). Routledge.
Woo, S. E., Jebb, A. T., Tay, L., & Parrigon, S. (2018). Putting the “Person” in the Center:Review and Synthesis of Person-Centered Approaches and Methods in Organizational Science. Organizational Research Methods, 21(4), 814-845. https://doi.org/10.1177/1094428117752467
Wu, H.-C., Chang, C.-Y., Chen, C.-L. D., Yeh, T.-K., & Liu, C.-C. (2010). Comparison of Earth Science Achievement Between Animation-Based and Graphic-Based Testing Designs. Research in Science Education, 40(5), 639-673. https://doi.org/10.1007/s11165-009-9138-9
Wu, H.-K., & Shah, P. (2004). Exploring visuospatial thinking in chemistry learning [https://doi.org/10.1002/sce.10126]. Science Education, 88(3), 465-492. https://doi.org/10.1002/sce.10126
Wu, H. K., & Hsieh, C. E. (2007). Developing Sixth Graders’ Inquiry Skills to Construct Explanations in Inquiry‐based Learning Environments. International Journal of Science Education, 28(11), 1289-1313. https://doi.org/10.1080/09500690600621035
Wu, Y. T., & Tsai, C. C. (2007). High School Students’ Informal Reasoning on a Socio‐scientific Issue: Qualitative and quantitative analyses. International Journal of Science Education, 29(9), 1163-1187. https://doi.org/10.1080/09500690601083375
Xu, Z., Zhao, Y., Liew, J., Zhou, X., & Kogut, A. (2023). Synthesizing research evidence on self-regulated learning and academic achievement in online and blended learning environments: A scoping review. Educational Research Review, 39, 100510. https://doi.org/https://doi.org/10.1016/j.edurev.2023.100510
Yan, Z., & Brown, G. T. L. (2017). A cyclical self-assessment process: towards a model of how students engage in self-assessment. Assessment & Evaluation in Higher Education, 42(8), 1247-1262. https://doi.org/10.1080/02602938.2016.1260091
Yan, Z., Lao, H., Panadero, E., Fernández-Castilla, B., Yang, L., & Yang, M. (2022). Effects of self-assessment and peer-assessment interventions on academic performance: A meta-analysis. Educational Research Review, 37, 100484. https://doi.org/https://doi.org/10.1016/j.edurev.2022.100484
Yang, F.-Y., Bhagat, K. K., & Cheng, C.-H. (2019). Associations of epistemic beliefs in science and scientific reasoning in university students from Taiwan and India. International Journal of Science Education, 41(10), 1347-1365. https://doi.org/10.1080/09500693.2019.1606960
Yang, J., Wang, Q., Wang, J., Huang, M., & Ma, Y. (2019). A study of K-12 teachers’ TPACK on the technology acceptance of E-schoolbag. Interactive Learning Environments, 29(7), 1062-1075. https://doi.org/10.1080/10494820.2019.1627560
Yen, M. H., Chen, S., Wang, C. Y., Chen, H. L., Hsu, Y. S., & Liu, T. C. (2018). A framework for self-regulated digital learning (SRDL). Journal of Computer Assisted Learning, 34(5), 580-589. https://doi.org/10.1111/jcal.12264
Yerdelen-Damar, S., Boz, Y., & Aydın-Günbatar, S. (2017). Mediated Effects of Technology Competencies and Experiences on Relations among Attitudes Towards Technology Use, Technology Ownership, and Self Efficacy about Technological Pedagogical Content Knowledge. Journal of Science Education and Technology, 26(4), 394-405. https://doi.org/10.1007/s10956-017-9687-z
Yi, H., Zhang, L., Luo, R., Shi, Y., Mo, D., Chen, X., . . . Rozelle, S. (2012). Dropping out: Why are students leaving junior high in China's poor rural areas? International Journal of Educational Development, 32(4), 555-563. https://doi.org/https://doi.org/10.1016/j.ijedudev.2011.09.002
Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449. https://doi.org/https://doi.org/10.1016/S1071-5819(03)00114-9
Yoon, H., Woo, A. J., Treagust, D., & Chandrasegaran, A. L. (2014). The Efficacy of Problem-based Learning in an Analytical Laboratory Course for Pre-service Chemistry Teachers. International Journal of Science Education, 36(1), 79-102. https://doi.org/10.1080/09500693.2012.727041
Zamora, Á., Suárez, J. M., & Ardura, D. (2016). Error detection and self-assessment as mechanisms to promote self-regulation of learning among secondary education students. The Journal of Educational Research, 111(2), 175-185. https://doi.org/10.1080/00220671.2016.1225657
Zeidler, D. L., & Nichols, B. H. (2009). Socioscientific issues: Theory and practice. Journal of Elementary Science Education, 21(2), 49-58. https://doi.org/10.1007/BF03173684
Zhang, H. (2017). Opportunity or new poverty trap: Rural-urban education disparity and internal migration in China. China Economic Review, 44, 112-124. https://doi.org/https://doi.org/10.1016/j.chieco.2017.03.011
Zheng, L. (2016). The effectiveness of self-regulated learning scaffolds on academic performance in computer-based learning environments: a meta-analysis. Asia Pacific Education Review, 17(2), 187-202. https://doi.org/10.1007/s12564-016-9426-9
Zhou, Q., Lee, C. S., & Sin, J. S.-C. (2021). When social media use for formal learning is voluntary : a study of students’ use of self-regulated learning strategies. Library and Information Science Research E-Journal, 31(1). https://doi.org/10.32655/libres.2021.1.2
Zhou, S., Han, J., Koenig, K., Raplinger, A., Pi, Y., Li, D., . . . Bao, L. (2016). Assessment of Scientific Reasoning: the Effects of Task Context, Data, and Design on Student Reasoning in Control of Variables. Think Skills Creat, 19, 175-187. https://doi.org/10.1016/j.tsc.2015.11.004
Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: a self-regulated learning perspective. Educational Technology Research and Development, 68(3), 1485-1519. https://doi.org/10.1007/s11423-020-09753-w
Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology, 11(4), 307-313. https://doi.org/https://doi.org/10.1016/0361-476X(86)90027-5
Zimmerman, B. J. (1990). Self-Regulating Academic Learning and Achievement: The Emergence of a Social Cognitive Perspective. Educational Psychology Review, 2(2), 173-201. http://www.jstor.org/stable/23359581
Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2
Zimmerman, B. J., & Pons, M. M. (1986). Development of a Structured Interview for Assessing Student Use of Self-Regulated Learning Strategies. American Educational Research Journal, 23(4), 614-628. https://doi.org/10.3102/00028312023004614
Zimmerman, C. (2007). The development of scientific thinking skills in elementary and middle school. Developmental Review, 27(2), 172-223. https://doi.org/10.1016/j.dr.2006.12.001
Zohar, A., & Barzilai, S. (2013). A review of research on metacognition in science education: current and future directions. Studies in Science Education, 49(2), 121-169. https://doi.org/10.1080/03057267.2013.847261