طراحی و اعتباریابی الگوی داربست سازی پویای رایانه‌ای در آموزش ضمن خدمت مجازی معلمان
محورهای موضوعی : روش های نوین آموزش وتوسعه منابع انسانی
زینب رشیدی
1
(دانشکده روانشناسی و علوم تربیتی دانشگاه علامه طباطبایی، تهران، ایران)
محمدرضا نیلی
2
(گروه تکنولوژی آموزشی، دانشکده روانشناسی و علوم تربیتی، دانشگاه علامه طباطبایی، تهران، ایران)
اسماعیل زارعی زوارکی
3
(گروه تکنولوژی آموزشی، دانشکده روانشناسی و علوم تربیتی، دانشگاه علامه طباطبایی، تهران، ایران)
علی دلاور
4
(گروه سنجش و اندازه¬گیری، دانشکده روانشناسی و علوم تربیتی، دانشگاه علامه طباطبایی، تهران، ایران)
کلید واژه: آموزش مجازی ضمن خدمت, داربست سازی پویا, داربست سازی رایانه‌ای, معلمان,
چکیده مقاله :
در آموزش مجازی ضمن خدمت معلمان پشتیبانی آموزشی یا داربست سازی یک جزء اساسی آموزش مؤثر محسوب می‌شود که می‌تواند به صورت داربست سازی رایانه‌ای ارائه شود. شخصی سازی داربست سازی رایانه‌ای با داربست سازی پویای رایانه‌ای محقق می‌شود. هدف این پژوهش طراحی و اعتباریابی الگوی داربست سازی پویای رایانه‌ای در آموزش ضمن خدمت مجازی معلمان می‌باشد. روش تحقیق مورد استفاده در این پژوهش، روش آمیخته با طرح متوالی اکتشافی بود. در تحقیق کیفی، روش سنتزپژوهی مورد استفاده قرار گرفت. در سنتزپژوهی، میدان پژوهش شامل اسناد و مدارک علمی معتبر پیرامون موضوع پژوهش بود که در ده پایگاه داده EBSCO، Google Scholar، Taylor & Francis، Emerald Insight، Scopus، Springer، ProQuest، ScienceDirect، Wiley، و SAGE در بازه زمانی 2015 تا 2020 مورد جستجو قرار گرفت. جستجوهای اینترنتی با کلمات کلیدی مرتبط انجام شد که 396 سند و مدرک علمی بدست آمد. در نهايت تعداد 133 نمونه که بیشترین هماهنگی و تناسب را با هدف این پژوهش داشتند به صورت هدفمند و طبق اشباع نظری داده ها انتخاب شدند. مطالعات انتخابی مورد تحلیل محتوای کیفی به شیوه استقرایی قرار گرفتند. سپس با كدگذاری باز و طبقه بندی آن‌ها، مؤلفه‌های الگو استخراج شدند و الگوی مفهومی طراحی گردید. تحقیق کمی برای اعتباربخشی الگو به کار رفت و از روش تحقیق توصیفی - تحلیلی استفاده شد. اساتید، دانشجویان و فارغ التحصیلان مقطع دکتری رشته تکنولوژی آموزشی، علوم رایانه، و سنجش و اندازه گیری، معلمانی که دوره‌های آموزش ضمن خدمت مجازی را گذرانده بودند و مدرسان و کارشناسان آموزش ضمن خدمت مجازی معلمان، جامعه آماری پژوهش با رویکرد کمی را تشکیل می‌دادند. به صورت هدفمند، 25 نفر به عنوان نمونه مورد نظر از میان آنان انتخاب شد. ابزار جمع آوری داده ها پرسشنامه محقق ساخته بود. روایی پرسشنامه توسط 3 نفر از متخصصان مورد تأیید قرار گرفت. هم چنین پایایی پرسشنامه با استفاده از روش آلفای کرونباخ 91/0 بدست آمد. برای تحلیل داده های کمی از شاخص‌های آمار توصیفی (فراوانی، میانگین و انحراف استاندارد) و در بخش آمار استنباطی از آزمون تی تک نمونه‌ای استفاده شد. یافته های اعتباریابی درونی از نظر متخصصان نشان داد که الگوی مفهومی از اعتبار درونی بالایی برخوردار می باشد. الگوی داربست سازی پویای رایانه‌ای در آموزش ضمن خدمت مجازی معلمان می‌تواند برای طراحی پشتیبانی آموزشی به صورت پویا و مبتنی بر رایانه به کار رود و منجر به یادگیری و عملکرد مستقل در آینده شود.
In virtual in-service teacher training, instructional support or scaffolding is an essential component of effective training that can be provided in the form of computer-based scaffolding. Personalization of computer-based scaffolding is achieved with dynamic computer-based scaffolding. The purpose of this study is to design and validate a model of dynamic computer-based scaffolding in virtual in-service teacher training. The research method used in this research was mixed method with sequential exploratory design. In qualitative research, the synthesis research method was used and the components of the model were extracted and a concept and process model was designed. Quantitative research was used to validate the model and survey research method and questionnaire were used and the internal validity of the proposed model was confirmed. Dynamic computer-based scaffolding model in virtual in-service teacher training has 8 main components and 47 sub-components. Key components include analysis with 11 subcomponents, design with 16 subcomponents, development with 3 subcomponents, implementation with 3 subcomponents, evaluation with 8 subcomponents, dissermination with 3 subcomponents, process evaluation and revision, and review and revision with 3 subcomponents. Internal validaty findings from experts showed that concept and process model have a high internal validity. Dynamic computer-based scaffolding model in online in-service teacher training can be used to design dynamic and computer-based instructional support and lead to learning and independent performance in the future.
1. Yarmohamadzadeh P, Yarigholi B, Doosti Alvanegh M. Examining teachers' perceptions of virtual in-service training. Quarterly Journal of Training and Development of Human Resources. 2021; 8(30): 134-160.
2. Nakhostin Maher L, Rahimi N, Ahmadi MM, Abdollahi D. the effectiveness of in-service virtual education in education. The 5th National Conference on Management Studies and Humanities in Iran 2018.
3. Hossini E, Tavayi A, Bibak E. Assessing the effectiveness of teachers’ in-service virtual training in salehabad (case study). The 4th Provincial Scientific Conference "From the Teacher" 2018.
4. Abbasian A. The effectiveness of virtual inservice training to improve job performance from the standpoint of high school teachers in the first period district five in Tehran city based on kirkpatrick's evaluation model. 2015. Master Thesis, Islamic Azad University of Central Tehran Branch.
5. Hakimzadeh R, Malekipour A, Malekipour M, Ghasempour E. Investigating the status of virtual courses of educators’ in-service training (a case study: educators of dehloran area). Information and Communication Technology in Educational Sciences. 2015; 5(4): 35-50.
6. Haddadian A. Surveying national e-learning system in the globalization era. Strategic Studies of Public Policy. 2011; 2(4): 117-148.
7. Shee DY, Wang YS. Multi-criteria evaluation of the web-based e-learning system: a methodology based on learner satisfaction and its applications. Computers & Education. 2008; 50(3): 894-905.
8. Wynants S, Dennis J. Professional development in an online context: opportunities and challenges from the voices of college faculty. Journal of Educators Online. 2018; 15(1): 1-13.
9. Yazdani F. Assessing the effectiveness of teachers’ in-service virtual training system. Information and Communication Technology in Educational Sciences. 2015; 2(18): 97-122.
10. Hoseinzadeh A. Assessing the attitude of shahriyar secondary school teachers toward in-service electronic courses. 2019. Master Thesis, Kharazmi University.
11. Jafari K, Mahdizadeh K, Sarshar F. Application of information and communication technology in in-service training of primary teachers: a new approach to teacher training. 4th National Conference on Novel Approaches to Education and Research 2019.
12. Little CA, Housand BC. Avenues to professional learning online: technology tips and tools for professional development in gifted education. Gifted Child Today. 2011; 34(4): 18-27.
13. Truong MT, Murray J. Understanding language teacher motivation in online professional development: a study of Vietnamese EFL teachers. The Electronic Journal for English as a Second Language. 2019; 24(3): 1-23.
14. Edinger MJ. Online teacher professional development for gifted education: examining the impact of a new pedagogical model. Gifted Child Quarterly. 2017; 61(4): 300–312.
15. Adnan Z. Upgrading EFL teachers’ quality through an online mentoring system, an innovative in-service training model: the case of Indonesia. Humaniora. 2018; 30(2): 158.
16. Rostaminezhad MA, Zaraii Zavaraki E, Mozayani N. Designing web-based instructions. 2016. Birjand: University of Birjand Press.
17. Simpson O. Supporting students in online, open and distance learning. 2018. Routledge.
18. Kim MC, Hannafin MJ. Scaffolding problem solving in technology-enhanced learning environments (TELEs): Bridging research and theory with practice. Computers & Education. 2011; 56(2): 403-417.
19. Puntambekar S, Hubscher R. Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed? Educational Psychologist. 2005; 40(1): 1-2.
20. Molenaar I, Roda C. Attention management for dynamic and adaptive scaffolding. Pragmatics & Cognition. 2008; 16(2): 224-271.
21. Vanderhoven E, Raes A, Schellens T. Increasing anonymity in peer assessment using an electronic voting system. In European Association for Research of Learning and Instruction (EARLI-2011) 2011.
22. Wu CH, Chen YS, Chen TG. An adaptive e-learning system for enhancing learning performance: Based on dynamic scaffolding theory. EURASIA Journal of Mathematics, Science and Technology Education. 2017; 14(3): 903-913.
23. Yelland N, Masters J. Rethinking scaffolding in the information age. Computers & Education. 2007; 48(3): 362-382.
24. Taghizade A, Aghakasiri Z. Scaffolding: A way for supporting learners in e-learning environments. Journal of Educational Studies. 2016; 8: 54-62.
25. Al Mamun MA, Lawrie G, Wright T. Instructional design of scaffolded online learning modules for self-directed and inquiry-based learning environments. Computers & Education. 2020; 144: 1-17.
26. Shin Y, Kim D, Song D. Types and timing of scaffolding to promote meaningful peer interaction and increase learning performance in computer-supported collaborative learning environments. Journal of Educational Computing Research. 2020; 58(3): 640-661.
27. Lee CB, Ling KV, Reimann P, Diponegoro YA, Koh CH, Chew D. Dynamic scaffolding in a cloud-based problem representation system: Empowering pre-service teachers’ problem solving. Campus-Wide Information Systems. 2014; 31(5): 346-356.
28. Yildiz I. Effects of scaffolding strategies embedded within webbased peer evaluation system on pre-service teachers’ reflective thinking and self-efficacy. 2012. Doctoral Dissertation, Middle East Technical University.
29. Wu B, Hu Y, Wang M. Scaffolding design thinking in online stem preservice teacher training. British Journal of Educational Technology. 2019; 50(5): 2271-2287.
30. Özçinar H. Scaffolding computer-mediated discussion to enhance moral reasoning and argumentation quality in pre-service teachers. Journal of Moral Education. 2015; 44(2): 232-251.
31. Lloyd M, Mukherjee M, Bellocchi A. Re-Imagining STEM: Peer scaffolding ICT in initial teacher education. 2nd International STEM in Education Conference 2012.
32. Lai G. Examining the effects of selected computer-based scaffolds on preservice teachers' levels of reflection as evidenced in their online journal writing. 2008. Doctoral Dissertation, Georgia State University.
33. Ramezani Ardi E, Zaraii Zavaraki E, Nili MR, Aliabadi K. Designing and validating the e-learning model in in-service training. Environmental Education and Sustainable Development. 2019; 7(2): 129-142.
34. Esfijani A. Quality indicators of virtual education: A meta-synthesis of approaches, criteria, and standards. Strides in Development of Medical Education. 2015; 12(1): 150-158.
35. Chen SY, Tseng YF. The impacts of scaffolding e-assessment English learning: A cognitive style perspective. Computer Assisted Language Learning. 2021; 34(8): 1105-1127.
36. Shin S, Brush TA, Glazewski KD. Designing and implementing web-based scaffolding tools for technology-enhanced socioscientific inquiry. Journal of Educational Technology & Society. 2017; 20(1): 1-2.
37. Bradley KS, Bradley JA. Scaffolding academic learning for second language learners. The Internet TESL Journal. 2004; 10(5): 16-18.
38. McCloskey ML, Orr J, Stack L, Kleckova G. Scaffolding academic language for english learners: What, why, how? 2010. Washington, DC: US Department of State.
39. Molenaar I, Roda C, Van Boxtel C, Sleegers P. Dynamic scaffolding of socially regulated learning in a computer-based learning environment. Computers & Education. 2012; 59(2): 515–523.
40. Molenaar I, Van Boxtel CA, Sleegers PJ. Metacognitive scaffolding in an innovative learning arrangement. Instructional Science. 2011; 39(6): 785-803.
41. Wolf MK, Guzman-Orth D, Lopez A, Castellano K, Himelfarb I, Tsutagawa FS. Integrating scaffolding strategies into technology-enhanced assessments of English learners: Task types and measurement models. Educational Assessment. 2016; 21(3): 157-175.
42. Wang TH. Developing an assessment-centered e-learning system for improving student learning effectiveness. Computers & Education. 2014; 73: 189-203.
43. Poehner ME, Lantolf JP. Vygotsky's teaching-assessment dialectic and L2 education: The case for dynamic assessment. Mind, Culture, and Activity. 2010; 17(4): 312-330.
44. Heuvel-Panhuizen MV, Kolovou A, Peltenburg M. Using ICT to improve assessment. In Assessment In The Mathematics Classroom: Yearbook 2011, Association of Mathematics Educators 2011.
45. Choi I, Wolf MK, Pooler E, Sova L, Faulkner-Bond M. Investigating the benefits of scaffolding in assessments of young english learners: A case for scaffolded retell tasks. Language Assessment Quarterly. 2019; 16(2): 161-179.
46. Almasri A, Ahmed A, Almasri N, Abu Sultan YS, Mahmoud AY, Zaqout IS, Akkila AN, Abu-Naser SS. Intelligent tutoring systems survey for the period 2000-2018. International Journal of Academic Engineering Research. 2019; 3(5): 21-37.
47. Bulut Özek M, Akpolat ZH, Orhan A. A web‐based intelligent tutoring system for a basic control course. Computer Applications in Engineering Education. 2013; 21(3): 561-571.
48. Rahimidoost G, Norozi D, Fardanesh H, Amirtymori MH. A framework for instructional scaffolding in computer-based and problem solving learning environment. Journal of Educational Scinces. 2013; 20(1): 243-268.
49. Huang Y, Xie Y, Qiu Y, Yuan Q, Liu Y, Zhong H. Research on support services of MOOC-based online teacher professional development during the COVID-19 pandemic. In 9th International Conference of Educational Innovation through Technology (EITT) 2020.
50. Irmer M, Traub D, Kramer M, Förtsch C, Neuhaus BJ. Scaffolding pre-service biology teachers’ diagnostic competences in a video-based Learning environment: Measuring the effect of different types of scaffolds. International Journal of Science Education. 2022: 1-21.
51. Asadi M, Gholami K. Synthesis research on an effective teaching model in higher education. Educational Planning Studies. 2016; 5(9): 113-144.
52. Bazargan A. An introduction to qualitative and mixed research methods: Common approaches in behavioral sciences. 2013. Tehran: Didar.
53. Elo S, Kyngäs H. The qualitative content analysis process. Journal of Advanced Nursing. 2008; 62(1): 107-115.
54. Hidayah I, Adji TB, Setiawan NA. A Framework for improving recommendation in adaptive metacognitive scaffolding. In 4th International Conference on Science and Technology (ICST) 2018.
55. Bhattacharya S, Chowdhury S, Roy S. An effective e-learning system through learners' scaffolding. International Journal of Advanced Intelligence Paradigms. 2018; 10(3): 290-304.
56. Chounta IA. Combining machine learning and learning analytics to provide personalized, adaptive scaffolding. A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings. 2019.
57. Jufriadi A, Ayu HD, Pratiw HY. Developing e-scaffolding integrated with e-assessment to improve student’s mastery of concept. In 1st International Conference on Education and Social Science Research 2019.
58. Valencia-Vallejo N, Lopez-Vargas O, Sanabria-Rodriguez L. Effect of a metacognitive scaffolding on self-efficacy, metacognition, and achievement in e-learning environments. Knowledge Management & E-Learning: An International Journal. 2019; 11(1): 1-9.
59. Rosen Y, Arieli-Attali M, Ward S, Seery J, Simmering V, Ozersky L, Stoeffler K, Webster K, von Davier A. HERA: Exploring the power of adaptive scaffolding on scientific argumentation and modelling competencies in online learning systems. In 14th International Conference of the Learning Sciences (ICLS) 2020.
60. Mironova O, Amitan I, Vendelin J, Vilipõld J, Saar M. Maximizing and personalizing e-learning support for students with different backgrounds and preferences. Interactive Technology and Smart Education. 2016.
61. Kim JY, Lim KY. Promoting learning in online, ill-structured problem solving: The effects of scaffolding type and metacognition level. Computers & Education. 2019; 138: 116-129.
62. Basu S, Biswas G. Providing adaptive scaffolds and measuring their effectiveness in open ended learning environments. Singapore: International Society of the Learning Sciences. 2016.
63. Bragg LA, Walsh C, Heyeres M. Successful design and delivery of online professional development for teachers: A systematic review of the literature. Computers & Education. 2021; 166: 104158.
64. Philipsen B, Tondeur J, McKenney S, Zhu C. Supporting teacher reflection during online professional development: A logic modelling approach. Technology, Pedagogy and Education. 2019; 28(2): 237-253.
65. Nurlayli A, Adji TB, Permanasari AE, Hidayah I. Tahani model of fuzzy database for an adaptive metacognitive scaffolding in hypermedia learning environment (case: Algorithm and structure data course). International Conference on Sustainable Information Engineering and Technology (SIET) 2017.
66. Powell CG, Bodur Y. Teachers’ perceptions of an online professional development experience: Implications for a design and implementation framework. Teaching and Teacher Education. 2019; 77: 19-30.
67. Korhonen AM, Ruhalahti S, Veermans M. The online learning process and scaffolding in student teachers’ personal learning environments. Education and Information Technologies. 2019; 24(1): 755-779.
68. Revilla Muñoz O, Alpiste Penalba F, Fernández Sánchez J. The skills, competences, and attitude toward information and communications technology recommender system: An online support program for teachers with personalized recommendations. New Review of Hypermedia and Multimedia. 2016; 22(1-2): 83-110.
69. Papazoglou, P., Psycharis, S., & Kalovrektis, K. Towards a dynamic multi-agent based scaffolding framework. International Journal of Circuits, Systems And Signal Processing. 2020; 14: 160-168.
70. Trust T, Pektas E. Using the ADDIE model and universal design for learning principles to develop an open online course for teacher professional development. Journal of Digital Learning in Teacher Education. 2018; 34(4): 219-233.
71. Lee K, Brett C. An online course design for inservice teacher professional development in a digital age: The effectiveness of the double-layered CoP model. In Handbook of Research on Teacher Education in the Digital Age 2015 (pp. 55-80). IGI Global.
72. Kim NJ, Belland BR, Lefler M, Andreasen L, Walker A, Axelrod D. Computer-based scaffolding targeting individual versus groups in problem-centered instruction for STEM education: Meta-analysis. Educational Psychology Review. 2020; 32(2): 415-461.
73. Modise MP. Continuous professional development and student support in an open and distance e-learning institution: A case study. International Journal of African Higher Education. 2020; 7(1).
74. Hidayah I, Adji TB, Setiawan NA. Development and evaluation of adaptive metacognitive scaffolding for algorithm‐learning system. IET Software. 2019; 13(4): 305-312.
75. Nurulsari N, Suyatna A. Development of soft scaffolding strategy to improve student’s creative thinking ability in physics. Journal of Physics: Conference Series 2017.
76. Reilly JM. Dynamic feedback as automated scaffolding to support learners and teachers in guided authentic scientific inquiry settings. 2020. Doctoral Dissertation, Harvard University.
77. González-Gómez D, Jeong JS. EdusciFIT: A computer-based blended and scaffolding toolbox to support numerical concepts for flipped science education. Education Sciences. 2019; 9(2): 116.
78. Hao S. Effects of faded scaffolding in computer-based instruction on learners' performance, cognitive load, and test anxiety. 2016. Doctoral Dissertation, The Florida State University.
79. Koes-H S, Suwasono P, Pramono NA. Efforts to improve problem solving abilities in physics through e-scaffolding in hybrid learning. In AIP Conference Proceedings 2019.
80. Daniel-Gittens KA, Calandrino T. Enhancing inquiry-based online teaching and learning: Integrating interactive technology tools to scaffold inquiry-based learning. In Inquiry-Based Learning for Multidisciplinary Programs: A Conceptual and Practical Resource for Educators 2015.
81. Van Bergen P, Lane R, Guilbert D. Enhancing pre-service teachers’ research engagement using flexible and scaffolded online resources. The Australian Educational Researcher. 2020; 47(4): 629-649.
82. Kim NJ. Enhancing students' higher order thinking skills through computer-based scaffolding in problem-based learning. 2017. Utah State University.
83. Shin S, Brush TA, Glazewski KD. Examining the hard, peer, and teacher scaffolding framework in inquiry-based technology-enhanced learning environments: Impact on academic achievement and group performance. Educational Technology Research and Development. 2020; 68(5): 2423-2447.
84. van der Graaf J, Molenaar I, Lim L, Fan Y, Engelmann K, Gašević D, Bannert M. Facilitating self-regulated learning with personalized scaffolds on student’s own regulation activities. International Learning and Analysis Conference 2020.
85. Martin ND, Dornfeld Tissenbaum C, Gnesdilow D, Puntambekar S. Fading distributed scaffolds: The importance of complementarity between teacher and material scaffolds. Instructional Science. 2019; 47(1): 69-98.
86. Bae H, Glazewski K, Brush T, Kwon K. Fostering transfer of responsibility in the middle school PBL classroom: An investigation of soft scaffolding. Instructional Science. 2021; 49(3): 337-363.
87. Lallé S, Mudrick NV, Taub M, Grafsgaard JF, Conati C, Azevedo R. Impact of individual differences on affective reactions to pedagogical agents scaffolding. International Conference on Intelligent Virtual Agents 2016.
88. Basu S, Biswas G, Kinnebrew JS. Learner modeling for adaptive scaffolding in a computational thinking-based science learning environment. User Modeling and User-Adapted Interaction. 2017; 27(1): 5-53.
89. Matsuda N, Sekar VP, Wall N. Metacognitive scaffolding amplifies the effect of learning by teaching a teachable agent. International Conference on Artificial Intelligence in Education 2018.
90. Munshi A, Rajendran R, Ocumpaugh J, Biswas G, Baker RS, Paquette L. Modeling learners' cognitive and affective states to scaffold SRL in open-ended learning environments. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization 2018.
91. Duffy MC, Azevedo R. Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior. 2015; 52: 338-348.
92. Cunha-Pérez C, Arevalillo-Herráez M, Marco-Giménez L, Arnau D. On incorporating affective support to an intelligent tutoring system: An empirical study. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje. 2018; 13(2): 63-69.
93. Csapó B, Molnár G. Online diagnostic assessment in support of personalized teaching and learning: The eDia system. Frontiers in Psychology. 2019; 10: 1522.
94. Pardo A, Bartimote K, Shum SB, Dawson S, Gao J, Gašević D, Leichtweis S, Liu D, Martínez-Maldonado R, Mirriahi N, Moskal AC. OnTask: Delivering data-informed, personalized learning support actions. Journal of Learning Analytics. 2018; 5(3): 235-249.
95. Wang S, Walker E, Chaudhry R, Wylie R. Personalized expert skeleton scaffolding in concept map construction. International Conference on Artificial Intelligence in Education 2015.
96. Amelia R, Rofiki I, Tortop HS, Abah JA. Pre-service teachers’ scientific explanation with e-scaffolding in blended learning. Jurnal Ilmiah Pendidikan Fisika Al Biruni. 2020; 9(1): 33-40.
97. Noroozi O, Kirschner PA, Biemans HJ, Mulder M. Promoting argumentation competence: Extending from first-to second-order scaffolding through adaptive fading. Educational Psychology Review. 2018; 30(1): 153-176.
98. Kay J, Kummerfeld B. Scaffolded, Scrutable Open Learner Model (SOLM) as a foundation for personalised e-textbooks. IniTextbooks@ AIED 2019 (pp. 38-43).
99. Li H, Gobert J, Dickler R. Scaffolding during science inquiry. In Proceedings of the 6th ACM Conference on Learning 2019.
100. Ertmer PA, Glazewski KD. Scaffolding in PBL environments: Structuring and problematizing relevant task features. The Wiley Handbook of Problem‐Based Learning. 2019; 3: 321-342.
101. Ge X, Chua BL. The role of self‐directed learning in PBL: Implications for learners and scaffolding design. The Wiley Handbook of Problem‐Based Learning. 2019; 3: 367-388.
102. Smit J, Gijsel M, Hotze A, Bakker A. Scaffolding primary teachers in designing and enacting language-oriented science lessons: Is handing over to independence a fata morgana? Learning, Culture and Social Interaction. 2018; 18: 72-85.
103. Peng J, Wang M, Sampson D. Scaffolding project-based learning of computer programming in an online learning environment. In IEEE 17th International Conference on Advanced Learning Technologies (ICALT) 2017.
104. Fernando W. Show me your true colours: Scaffolding formative academic literacy assessment through an online learning platform. Assessing Writing. 2018; 36: 63-76.
105. Fiechter JL, Benjamin AS. Techniques for scaffolding retrieval practice: The costs and benefits of adaptive versus diminishing cues. Psychonomic Bulletin & Review. 2019; 26(5): 1666-1674.
106. Matsuda N, Weng W, Wall N. The effect of metacognitive scaffolding for learning by teaching a teachable agent. International Journal of Artificial Intelligence in Education. 2020; 30(1): 1-37.
107. Kern CL, Crippen KJ. The effect of scaffolding strategies for inscriptions and argumentation in a science cyberlearning environment. Journal of Science Education and Technology. 2017; 26(1): 33-43.
108. Svetsky S, Moravcik O, Tanuska P, Markechova I. The personalized computer support of teaching. International Journal of Engineering Pedagogy (iJEP). 2018; 8(4): 56-69.
109. Ayedoun E, Hayashi Y, Seta K. Toward personalized scaffolding and fading of motivational support in L2 learner–dialogue agent interactions: An exploratory study. IEEE Transactions on Learning Technologies. 2020; 13(3): 604-616.
110. Krishna S, Pelachaud C, Kappas A. Towards an adaptive regulation scaffolding through role-based strategies. In Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents 2019.
111. Rashid AH, Shukor NA, Tasir Z. Using computer-based scaffolding to improve students' reasoning skills in collaborative learning. In IEEE 8th International Conference on Engineering Education (ICEED) 2016.
112. Bennison A, Goos M, Geiger V. Utilising a research-informed instructional design approach to develop an online resource to support teacher professional learning on embedding numeracy across the curriculum. ZDM. 2020; 52(5): 1017-1031.
113. Hsu YS, Lai TL, Hsu WH. A design model of distributed scaffolding for inquiry-based learning. Research in Science Education. 2015; 45(2): 241-273.
114. Jiménez S, Juárez‐Ramírez R, Castillo VH, Licea G, Ramírez‐Noriega A, Inzunza S. A feedback system to provide affective support to students. Computer Applications in Engineering Education. 2018; 26(3): 473-483.
115. O'Rourke E, Andersen E, Gulwani S, Popović Z. A framework for automatically generating interactive instructional scaffolding. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems 2015.
116. Gulzar Z, Leema AA. A framework for recommender system to support personalization in an e-learning system. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT). 2018; 13(3): 51-68.
117. Jumaat NF, Tasir Z. A framework of metacognitive scaffolding in learning authoring system through Facebook. Journal of Educational Computing Research. 2016; 54(5): 619-659.
118. Vista A, Care E, Griffin P. A new approach towards marking large-scale complex assessments: Developing a distributed marking system that uses an automatically scaffolding and rubric-targeted interface for guided peer-review. Assessing Writing. 2015; 24: 1-5.
119. Belland BR, Walker AE, Olsen MW, Leary H. A pilot meta-analysis of computer-based scaffolding in STEM education. Journal of Educational Technology & Society. 2015; 18(1): 183-197.
120. Su JM. A rule‐based self‐regulated learning assistance scheme to facilitate personalized learning with adaptive scaffoldings: A case study for learning computer software. Computer Applications in Engineering Education. 2020; 28(3): 536-555.
121. Su JM. A self-regulated learning tutor to adaptively scaffold the personalized learning: A study on learning outcome for grade 8 Mathematics. In 8th International Conference on Ubi-Media Computing (UMEDIA) 2015.
122. Jeong JS, Ramírez-Gómez Á, González-Gómez D. A web-based scaffolding-learning tool for design students’ sustainable spatial planning. Architectural Engineering and Design Management. 2017; 13(4): 262-277.
123. Deejring K. The validation of web-based learning using collaborative learning techniques and a scaffolding system to enhance learners’ competency in higher education. Procedia-Social and Behavioral Sciences. 2015; 174: 34-42.
124. Majumdar R, Yang YY, Li H, Akçapınar G, Flanagan B, Ogata H. Adaptive support for acquisition of self-direction skills using learning and health data. In IEEE 19th International Conference on Advanced Learning Technologies 2019.
125. Arevalillo-Herraez M, Marco-Gimenez L, Arnau D, Gonzalez-Calero JA. Adding sensor-free intention-based affective support to an Intelligent Tutoring System. Knowledge-Based Systems. 2017; 132: 85-93.
126. Wambsganss T, Niklaus C, Cetto M, Söllner M, Handschuh S, Leimeister JM. AL: An adaptive learning support system for argumentation skills. In Proceedings of the CHI Conference on Human Factors in Computing Systems 2020.
127. Belland BR, Weiss DM, Kim NJ, Piland J, Gu J. An examination of credit recovery students’ use of computer-based scaffolding in a problem-based, scientific inquiry unit. International Journal of Science and Mathematics Education. 2019; 17(2): 273-293.
128. Kaoropthai C, Natakuatoong O, Cooharojananone N. An intelligent diagnostic framework: A scaffolding tool to resolve academic reading problems of Thai first-year university students. Computers & Education. 2019; 128: 132-144.
129. Kochmar E, Vu DD, Belfer R, Gupta V, Serban IV, Pineau J. Automated personalized feedback improves learning gains in an intelligent tutoring system. In International Conference on Artificial Intelligence in Education 2020.
130. Bimba AT, Idris N, Al-Hunaiyyan A, Mahmud RB, Shuib NL. Adaptive feedback in computer-based learning environments: A review. Adaptive Behavior. 2017; 25(5): 217-234.
131. Belland BR. Computer-based scaffolding strategy. In Instructional scaffolding in STEM education 2017 (pp. 107-126). Springer, Cham.
132. Belland BR. Context of use of computer-based scaffolding. In Instructional scaffolding in STEM education 2017 (pp. 55-77). Springer, Cham.
133. Belland BR, Kim C, Lee E. Customized scaffolding for pre-service teachers’ problem-solving in STEM. In Annual meeting program American Educational Research Association 2020.
134. Lopez AJ, Zorrozua IL, Ruiz ES, Rodríguez-Artacho M, Gil MC. Design and development of a responsive web application based on scaffolding learning. In IEEE Global Engineering Education Conference (EDUCON) 2016.
135. Gumilar AG, Sunarya Y, Arifin M. Developing chemistry teacher’s ability to design inquiry-based lab through scaffolding type of teacher training program. In Journal of Physics: Conference Series 2017.
136. Saputri AA, Wilujeng I. Developing physics e-scaffolding teaching media to increase the eleventh-grade students' problem solving ability and scientific attitude. International Journal of Environmental and Science Education. 2017; 12(4): 729-745.
137. Velázquez AF, Perales JN, Pérez FC. Development of an intelligent tutoring system of generalized support for differentiated learning. In 11th International Conference on Education and New Learning Technologies 2019.
138. Ustunel HH, Tokel ST. Distributed scaffolding: Synergy in technology-enhanced learning environments. Technology, Knowledge and Learning. 2018; 23(1): 129-160.
139. Abdelaziz HA, Al Zehmi O. E-cognitive scaffolding: Does it have an impact on the English grammar competencies of middle school underachieving students? Open Learning: The Journal of Open, Distance and e-Learning. 2021; 36(1): 5-28.
140. Saâdi IB, Bayounes W, Ben Ghezala H. Educational processes’ guidance based on evolving context prediction in intelligent tutoring systems. Universal Access in the Information Society. 2020; 19(4): 701-724.
141. O'Connor E, McDonald F, Ruggiero M. Scaffolding complex learning: Integrating 21st century thinking, emerging technologies, and dynamic design and assessment to expand learning and communication opportunities. Journal of Educational Technology Systems. 2014; 43(2): 199-226.
142. Kim NJ, Belland BR, Walker AE. Effectiveness of computer-based scaffolding in the context of problem-based learning for STEM education: Bayesian meta-analysis. Educational Psychology Review. 2018; 30(2): 397-429.
143. Laksitowening KA, Yanuarifiani AP, Wibowo YF. Enhancing e-learning system to support learning style based personalization. In 2nd International Conference on Science in Information Technology (ICSITech) 2016.
144. Asselman A, Khaldi M, Aammou S. Evaluating the impact of prior required scaffolding items on the improvement of student performance prediction. Education and Information Technologies. 2020; 25(4): 3227-3249.
145. Sun JC, Yu SJ, Chao CH. Effects of intelligent feedback on online learners’ engagement and cognitive load: The case of research ethics education. Educational Psychology. 2019; 39(10): 1293-1310.
146. Li H, Gobert J, Dickler R. Evaluating the transfer of scaffolded inquiry: What sticks and does it last? In International Conference on Artificial Intelligence in Education 2019.
147. Dlab MH. Experiences in using educational recommender system ELARS to support e-learning. In 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) 2017.
148. Jamari D, Zaid NM, Abdullah Z, Mohamed H, Aris B. Instructional scaffolding to support ill-structured problem solving: A review. Sains Humanika. 2017; 9(1-4).
149. Shpolianskaya I, Seredkina T. Intelligent support system for personalized online learning. Broad Research in Artificial Intelligence and Neuroscience. 2020; 11(3): 29-35.
150. Belland BR. Intended learning outcomes and assessment of computer-based scaffolding. In Instructional Scaffolding in STEM Education 2017 (pp. 79-106). Springer, Cham.
151. González‐Calero JA, Arnau D, Puig L, Arevalillo‐Herráez M. Intensive scaffolding in an intelligent tutoring system for the learning of algebraic word problem solving. British Journal of Educational Technology. 2015; 46(6): 1189-1200.
152. Thomas AF, Sondergeld T. Investigating the impact of feedback instruction: Partnering preservice teachers with middle school students to provide digital, scaffolded feedback. Journal of the Scholarship of Teaching and Learning. 2015; 28: 83-109.
153. Edson AJ. Learner-controlled scaffolding linked to open-ended problems in a digital learning environment. ZDM. 2017; 49(5): 735-753.
154. Pezzino M. Online assessment, adaptive feedback and the importance of visual learning for students. The advantages, with a few caveats, of using MapleTA. International Review of Economics Education. 2018; 28: 11-28.
155. Munshi A, Biswas G. Personalization in OELEs: Developing a data-driven framework to model and scaffold SRL processes. In International Conference on Artificial Intelligence in Education 2019.
156. Xia J, Li G, Cao Z. Personalized exercise recommendation algorithm combining learning objective and assignment feedback. Journal of Intelligent & Fuzzy Systems. 2018; 35(3): 2965-2973.
157. Ueno M, Miyasawa Y. Probability based scaffolding system with fading. In International Conference on Artificial Intelligence in Education 2015.
158. Saman MI, Koes-H S, Sunaryono S. Procedural e-scaffolding in improving students' physics problem solving skills. Unnes Science Education Journal. 2018; 7(2).
159. Dudyrev F, Maksimenkova O, Neznanov A. Providing cognitive scaffolding within computer-supported adaptive learning environment for material science education. In International Conference on Interactive Collaborative Learning 2018.
160. Albacete P, Jordan P, Lusetich D, Chounta IA, Katz S, McLaren BM. Providing proactive scaffolding during tutorial dialogue using guidance from student model predictions. In International Conference on Artificial Intelligence in Education 2018.
161. Cheng HN, Yang EF, Liao CC, Chang B, Huang YC, Chan TW. Scaffold seeking: A reverse design of scaffolding in computer-supported word problem solving. Journal of Educational Computing Research. 2015; 53(3): 409-435.
162. Moore EB, Mäeots M, Smyrnaiou Z. Scaffolding for inquiry learning in computer-based learning environments. In New Developments in Science and Technology Education 2016.
163. van Dijk AM, Lazonder AW. Scaffolding students' use of learner-generated content in a technology-enhanced inquiry learning environment. Interactive Learning Environments. 2016; 24(1): 194-204.
164. Oktavianti E, Handayanto SK, Wartono W, Saniso E. Students scientific explanation in blended physics learning with e-scaffolding. Jurnal Pendidikan IPA Indonesia. 2018; 7(2): 181-186.
165. Belland BR, Walker AE, Kim NJ, Lefler M. Synthesizing results from empirical research on computer-based scaffolding in STEM education: A meta-analysis. Review of Educational Research. 2017; 87(2): 309-344.
166. Setiawan T. The exploration of using e-scaffolding in solving physics problem. In 4th International Conference on Science and Technology 2018.
167. Albacete P, Jordan P, Katz S, Chounta IA, McLaren BM. The impact of student model updates on contingent scaffolding in a natural-language tutoring system. In International Conference on Artificial Intelligence in Education 2019.
168. Ayu HD, Jufriadi A. The implication of e-scaffolding in mathematical physics students achievement and motivation. In Proceedings of the Annual Conference on Social Sciences and Humanities 2018.
169. Lämsä J, Hämäläinen R, Koskinen P, Viiri J, Mannonen J. The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes. Computers & Education. 2020; 143: 103674.
170. Bywater JP, Chiu JL, Hong J, Sankaranarayanan V. The teacher responding tool: Scaffolding the teacher practice of responding to student ideas in mathematics classrooms. Computers & Education. 2019; 139: 16-30.
171. Law V, Ge X, Huang K. Understanding learners’ challenges and scaffolding their ill-structured problem solving in a technology-supported self-regulated learning environment. In Handbook of Research in Educational Communications and Technology 2020 (pp. 321-343). Springer, Cham.
172. Şendurur E, Yildirim Z. Web-based metacognitive scaffolding for internet search. Journal of Educational Technology Systems. 2019; 47(3): 433-455.
173. Narciss S, Sosnovsky S, Schnaubert L, Andrès E, Eichelmann A, Goguadze G, Melis E. Exploring feedback and student characteristics relevant for personalizing feedback strategies. Computers & Education. 2014; 71: 56-76.
174. Belland BR, Gu J, Armbrust S, Cook B. Using generic and context-specific scaffolding to support authentic science inquiry. International Association for Development of the Information Society 2013.
175. Brush TA, Saye JW. A summary of research exploring hard and soft scaffolding for teachers and students using a multimedia supported learning environment. The Journal of Interactive Online Learning. 2002; 1(2): 1-2.
176. Quintana C, Reiser BJ, Davis EA, Krajcik J, Fretz E, Duncan RG, Kyza E, Edelson D, Soloway E. A scaffolding design framework for software to support science inquiry. In The Journal of the Learning Sciences 2004 (pp. 337-386). Psychology Press.
177. Rubio DM, Berg-Weger M, Tebb SS, Lee ES, Rauch S. Objectifying content validity: Conducting a content validity study in social work research. Social Work Research. 2003; 27(2): 94-104.
178. Kim NJ, Belland BR, Axelrod D. Scaffolding for optimal challenge in k–12 problem-based learning. The Interdisciplinary Journal of Problem-based Learning. 2018.
179. Sacchanand C. Information literacy instruction to distance students in higher education: librarians’ key role. In 68th IFLA Council and General Conference 2002.
180. Bagheri Majd R, Seyed Abbaszadeh M, Mehr Alizadeh Y, Shahi S. Studying and designing the pedagogical pattern in virtual higher education. Information and Communication Technology in Educational Sciences. 2014; 3(15): 127-144.
181. Delen E, Liew J, Willson V. Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments. Computers & Education. 2014; 78: 312-320.