ThinkDeeper Web Coach: A Literature-Informed Design Specification for Process-Visible Reasoning in Problem-Solving-Intensive Information Technology Courses
DOI:
https://doi.org/10.34148/teknika.v15i2.1504Keywords:
Depth of Thinking, Process-visible Learning, Information Technology Education, Learning Analytics, ScaffoldingAbstract
Information technology education increasingly requires students to demonstrate how they reason while generative tools can produce plausible code, queries, and explanations with little visible evidence of the learner's own computational thinking. This study aimed to design ThinkDeeper Web Coach, a process-visible learning artifact for problem-solving-intensive IT courses that requires structured reasoning before direct solution support is accessed. A literature-informed design and development approach was applied to academic records indexed in Semantic Scholar and OpenAlex. The evidence corpus comprised 500 records published from 2023 to 2025, 130 records retained after abstract screening, 53 full texts assessed, and 51 studies included after full-text screening and extraction. The synthesis produced the Process-Visible Learning Design Framework, eight evidence-linked design requirements, and six integrated modules: Answer Delay Gate, Coach Chat, Evidence Drawer, Depth Analytics, Learning Circle, and Resource Recommendation. The revised specification defines applicable course contexts, operational dimensions of depth of thinking, a hybrid reference architecture, server-side gate and guardrail mechanics, trace data objects, and a staged evaluation protocol. ThinkDeeper is presented as a theoretically and empirically justified design specification that still requires expert review, usability testing, trace validation, and classroom pilot evaluation before effectiveness claims can be made.
Downloads
References
[1] L. T. Ameen, M. R. Yousif, N. A. J. Alnoori, and B. H. Majeed, “The Impact of Artificial Intelligence on Computational Thinking in Education at University,” Int. J. Eng. Pedagogy, vol. 14, no. 5, pp. 192–203, 2024, doi: 10.3991/ijep.v14i5.49995.
[2] H.-L. Liu, C.-F. Lai, and H.-C. K. Lin, “Effects of Facial Recognition and Text Semantic Recognition on Affective Tutoring System,” J. Internet Technol., vol. 25, no. 6, pp. 807–814, 2024, doi: 10.70003/160792642024112506001.
[3] M. Lu and Z. Hu, “Leveraging Multimodal Information for Web Front-End Development Instruction: Analyzing Effects on Cognitive Behavior, Interaction, and Persistent Learning,” Information, vol. 16, no. 9, p. 734, 2025, doi: 10.3390/info16090734.
[4] R. H. Sakti et al., “Diving into the Future: Unravelling the Impact of Flowgorithm and Discord Fusion on Algorithm and Programming Courses and Fostering Computational Thinking,” Int. J. Learn. Teach. Educ. Res., vol. 23, no. 7, pp. 347–367, 2024, doi: 10.26803/ijlter.23.7.18.
[5] Y. Xu, J. Zhu, M. Wang, F. Qian, Y. Yang, and J. Zhang, “The Impact of a Digital Game-Based AI Chatbot on Students’ Academic Performance, Higher-Order Thinking, and Behavioral Patterns in an Information Technology Curriculum,” Appl. Sci., vol. 14, no. 15, p. 6418, 2024, doi: 10.3390/app14156418.
[6] C.-C. Lee and M. Y. H. Low, “Using GenAI in Education: The Case for Critical Thinking,” Front. Artif. Intell., vol. 7, p. 1452131, 2024, doi: 10.3389/frai.2024.1452131.
[7] Q. Liu and C.-C. Tu, “Improving Critical Thinking Through AI-Supported Socio-Scientific Issues Instruction,” J. Logist. Inform. Serv. Sci., vol. 11, no. 3, pp. 52–65, 2024, doi: 10.33168/JLISS.2024.0304.
[8] S. Ma, J. Wang, Y. Zhang, X. Ma, and A. Y. Wang, “DBox: Scaffolding Algorithmic Programming Learning Through Learner-LLM Co-Decomposition,” in Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, in CHI ’25. New York, NY, USA: Association for Computing Machinery, 2025. doi: 10.1145/3706598.3713748.
[9] P. Maiti and A. K. Goel, “How Do Students Interact with an LLM-Powered Virtual Teaching Assistant in Different Educational Settings?” 2024. doi: 10.48550/arXiv.2407.17429.
[10] Q. Pan et al., “AMQuestioner: Training Critical Thinking with Question-Driven Interactive Argument Maps in Online Discussion,” Proc. ACM Hum.-Comput. Interact., vol. 9, no. CSCW, 2025, doi: 10.1145/3757551.
[11] S. Park, H. Subramonyam, and C. Kulkarni, “Thinking Assistants: LLM-Based Conversational Assistants That Help Users Think by Asking Rather Than Answering.” 2023. doi: 10.48550/arXiv.2312.06024.
[12] C. Borchers, J. Zhang, R. S. Baker, and V. Aleven, “Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems,” in LAK ’24: Proceedings of the 14th Learning Analytics and Knowledge Conference, New York, NY, USA: Association for Computing Machinery, 2024, pp. 529–539. doi: 10.1145/3636555.3636911.
[13] V. Dornauer, M. Netzer, É. Kaczkó, L.-M. Norz, and E. Ammenwerth, “Automatic Classification of Online Discussions and Other Learning Traces to Detect Cognitive Presence,” Int. J. Artif. Intell. Educ., vol. 34, pp. 395–415, 2024, doi: 10.1007/s40593-023-00335-4.
[14] G. Ramaswami, T. Sušnjak, and A. Mathrani, “Effectiveness of a Learning Analytics Dashboard for Increasing Student Engagement Levels,” J. Learn. Anal., vol. 10, no. 3, pp. 115–134, 2023, doi: 10.18608/jla.2023.7935.
[15] V. Serrano, J. Cuadros, L. Fernández-Ruano, J. García-Zubía, U. Hernández-Jayo, and F. Lluch, “Learning Analytics Dashboards for Assessing Remote Labs Users’ Work: A Case Study with VISIR-DB,” Technol. Knowl. Learn., vol. 30, no. 1, pp. 263–290, 2025, doi: 10.1007/s10758-024-09752-3.
[16] G. Guo, A. M. S. Kumar, A. Gupta, A. J. Coscia, C. J. MacLellan, and A. Endert, “Visualizing Intelligent Tutor Interactions for Responsive Pedagogy,” in Proceedings of the 2024 International Conference on Advanced Visual Interfaces, in AVI ’24. New York, NY, USA: Association for Computing Machinery, 2024. doi: 10.1145/3656650.3656667.
[17] P. Utamachant, C. Anutariya, and S. Pongnumkul, “i-Ntervene: Applying an Evidence-Based Learning Analytics Intervention to Support Computer Programming Instruction,” Smart Learn. Environ., vol. 10, no. 1, 2023, doi: 10.1186/s40561-023-00257-7.
[18] Y. Uzun, W. Suraworachet, Q. Zhou, A. Gauthier, and M. Cukurova, “Engagement with Analytics Feedback and Its Relationship to Self-Regulated Learning Competence and Course Performance,” Int. J. Educ. Technol. High. Educ., vol. 22, no. 1, 2025, doi: 10.1186/s41239-025-00515-3.
[19] G. L. Akinyi, R. O. Oboko, and L. Muchemi, “Learning Analytics Intervention Using Prompts and Feedback for Measurement of e-Learners’ Socially-Shared Regulated Learning,” Electron. J. E-Learn., vol. 22, no. 5, pp. 103–116, 2024, doi: 10.34190/ejel.22.5.3253.
[20] R. A. Lobo-Quintero, “AI-Enhanced Think-Pair-Share: A Learning Analytics Approach to Foster Linguistic Creative Thinking and Collaborative Learning,” J. Learn. Anal., vol. 12, no. 2, pp. 19–34, 2025, doi: 10.18608/jla.2025.8807.
[21] M. Mielikäinen and E. Viippola, “ICT Engineering Students’ Perceptions on Project-Based Online Learning in Community of Inquiry (CoI),” SAGE Open, vol. 13, no. 3, 2023, doi: 10.1177/21582440231180602.
[22] G. Psathas, S. Tegos, S. N. Demetriadis, and T. Tsiatsos, “Exploring the Impact of Chat-Based Collaborative Activities and SRL-Focused Interventions on Students’ Self-Regulation Profiles, Participation in Collaborative Activities, Retention, and Learning in MOOCs,” Int. J. Comput.-Support. Collab. Learn., vol. 18, pp. 329–351, 2023, doi: 10.1007/s11412-023-09394-0.
[23] T. T. Dien, T.-H. Nguyen, and T.-N. Nguyen, “Novel Approaches for Searching and Recommending Learning Resources,” Cybern. Inf. Technol., vol. 23, no. 2, pp. 151–169, 2023, doi: 10.2478/cait-2023-0019.
[24] M. Domino and C. A. Shaffer, “Using a Wider Digital Ecosystem to Improve Self-Regulated Learning,” Front. Educ., vol. 10, p. 1487344, 2025, doi: 10.3389/feduc.2025.1487344.
[25] T. Pham-Duc, “PERS: A Personalized Recommender System for Student-Generated Questions in Programming Courses,” in Proceedings of the International Conference on Computers in Education, 2024. doi: 10.58459/icce.2024.4913.
[26] J.-W. Tzeng, N.-F. Huang, A.-C. Chuang, T.-W. Huang, and H.-Y. Chang, “Massive Open Online Course Recommendation System Based on a Reinforcement Learning Algorithm,” Neural Comput. Appl., vol. 37, pp. 11607–11618, 2023, doi: 10.1007/s00521-023-08686-8.
[27] S. McKenney and T. C. Reeves, “Educational Design Research: Portraying, Conducting, and Enhancing Productive Scholarship,” Med. Educ., vol. 55, no. 1, pp. 82–92, 2021, doi: 10.1111/medu.14280.
[28] N. Norouzi and A. Mazaheri, “Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2023, pp. 15938–15946. doi: 10.1609/aaai.v37i13.26892.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Teknika

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.















