IEEE Transactions on Learning Technologies最新文献_第5页

Increased Immersion in the Digital Multiverse: Implications for Adult Motivation 增加沉浸在数字多元宇宙：对成人动机的影响

IF 7.4 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-07-09 DOI: 10.1109/TLT.2026.3711900

Chris Dede

{"title":"Increased Immersion in the Digital Multiverse: Implications for Adult Motivation","authors":"Chris Dede","doi":"10.1109/TLT.2026.3711900","DOIUrl":"https://doi.org/10.1109/TLT.2026.3711900","url":null,"abstract":"People who often virtually enhance their physical context are part of the digital multiverse, which has greatly expanded in scope and depth in the last few decades due to e-commerce, streaming entertainment, video gaming, social media, telehealth services, videoconferencing, and online learning. In recent years, most adults active in the digital multiverse report that their level of virtual activity has risen substantially compared to prepandemic life. Spending a high proportion of daily time in virtual environments and simulated experiences may result in social phobia, complexes, or loss of basic skills to function in the real world. Loneliness may be enhanced by the inability of remote interactions to substitute for the full advantages of face-to-face experiences. People who are co-located in the real world may have their interpersonal connections undercut through their individual patterns of virtual immersion. As advances in Artificial Intelligence and XR further increase the scope and depth of the digital multiverse ecosystem through widespread bottom-up authoring of simulated experiences, protecting adults from misinformation and malicious actions may be challenging, but it is important to minimize risks and enhance benefits. Research on these problems and strategies for alleviating them is an important frontier for adult well-being.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"706-710"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gamification of Competence Development: A Study of Gamified Support for Higher Education and Lifelong Learning 能力发展的游戏化：高等教育与终身学习的游戏化支持研究

IF 7.4 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-06-15 DOI: 10.1109/TLT.2026.3703850

Miiko Siivonen;Henri Pirkkalainen;Bakhtawar Khan;Nannan Xi

{"title":"Gamification of Competence Development: A Study of Gamified Support for Higher Education and Lifelong Learning","authors":"Miiko Siivonen;Henri Pirkkalainen;Bakhtawar Khan;Nannan Xi","doi":"10.1109/TLT.2026.3703850","DOIUrl":"https://doi.org/10.1109/TLT.2026.3703850","url":null,"abstract":"Rapid changes in the labour market have made reskilling and upskilling more relevant and important for career development than ever before. As the number of ways to obtain new skills and competences increases, so does the need for individual learning paths and support. As one of the most representative motivation and persuasive information systems, gamification has been employed as one potential approach for self-growth and skill development. This article examines how different learners, i.e., students in higher education and lifelong learners who develop their skills and competences in work-setting, value different gamified support mechanisms for individual competence development. For the study, a novel framework was developed that separates gamified support for competence development into two dimensions of temporality and openness. Utilizing the framework, a within-subject vignette-based survey study was conducted with two learner groups (higher education <italic>N</i> = 544, lifelong learner <italic>N</i> = 285). Statistical analysis revealed that learners in higher education perceived overall value, i.e., usefulness, enjoyment and playfulness, in long-term gamified support that allows flexibility and freedom to explore new learning opportunities. Lifelong learners perceived value in similar guidance but perceived additional utility in shorter-term gamified support that either guides them toward specific skills the learner should focus on or allows a flexible exploration and acquisition of skills at the learner's own pace.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"620-635"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11563876","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Collaborative Problem Solving Through AI Personal Assistant Interactions: Insights From Diverse Prior Knowledge Groups 通过人工智能个人助理互动增强协作解决问题：来自不同先验知识群体的见解

IF 7.4 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-07-15 DOI: 10.1109/TLT.2026.3713908

Yeye Tang;Xu Du;Jui-Long Hung;Minjuan Wang;Tingyin Ding

{"title":"Enhancing Collaborative Problem Solving Through AI Personal Assistant Interactions: Insights From Diverse Prior Knowledge Groups","authors":"Yeye Tang;Xu Du;Jui-Long Hung;Minjuan Wang;Tingyin Ding","doi":"10.1109/TLT.2026.3713908","DOIUrl":"https://doi.org/10.1109/TLT.2026.3713908","url":null,"abstract":"Collaborative problem-solving (CPS) activities encourage learners to share knowledge and complete complex tasks together. However, learners with insufficient prior knowledge may struggle to contribute effectively to these tasks. Appropriate support is, therefore, needed to help learners address challenges that arise during CPS. This study developed an AI personal assistant (AIPA) embedded with a ChatGPT-based large language model to serve as a personal consultant in CPS activities by providing personalized knowledge, resources, and guidance. This design raises an important question about how AI assistance is associated with learners’ interaction patterns and the quality of collaboration. By analyzing student dialogues and interactions with the AIPA, this study found no significant differences in CPS performance among high-, mixed-, and low-knowledge groups after using the AIPA. However, both mixed- and low-knowledge groups interacted with the AIPA more frequently than the high-knowledge groups. Analysis of question types further showed significant differences, with explanation-oriented questions being more common in low-knowledge groups. In addition, high-knowledge groups interacted minimally with the AIPA, low-knowledge groups engaged consistently, and mix-knowledge groups showed moderate interaction, with shifting question types across the CPS process. The results also showed that groups with lower prior knowledge relied more on information provided by the AIPA in their discussions. These findings suggest that the AIPA can serve as a supportive resource in CPS contexts and contribute to understanding students’ interaction patterns with AI support in educational settings.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"766-777"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Auditing the Reliability of Automated Risk Evaluators in Agentic Tutoring Systems Under Pedagogical Style Variations 教学风格变化下代理辅导系统中自动风险评估器的可靠性审计

IF 7.4 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-07-24 DOI: 10.1109/TLT.2026.3716609

Wenzhuo Song;Xuechen Wang;Shuang Liang;Jianan Wang

{"title":"Auditing the Reliability of Automated Risk Evaluators in Agentic Tutoring Systems Under Pedagogical Style Variations","authors":"Wenzhuo Song;Xuechen Wang;Shuang Liang;Jianan Wang","doi":"10.1109/TLT.2026.3716609","DOIUrl":"https://doi.org/10.1109/TLT.2026.3716609","url":null,"abstract":"Agentic tutoring systems increasingly rely on large language models (LLMs) to generate real-time feedback, yet risky outputs, such as answer leakage, contradiction, and hallucination, can directly affect learning. Answer leakage is a central concern in this setting. It exposes problem solutions to students and can be verified through human annotation. This article presents a pedagogical style-conditioned audit protocol for automated evaluators that detect answer leakage in LLM-generated tutoring feedback before deployment. Using 1100 mathematics problems from the MATH dataset and 4400 feedback items generated under Baseline, Direct, Socratic, and Encouragement styles, we compare rule-based, embedding-based, natural language inference-based, and two LLM-as-a-Judge evaluators from different model families (Qwen2.5-32B and Gemma3-27B) and validate them against a 314-item human-annotated gold standard. The results show that pedagogical style changes how answer-leakage signals appear in feedback and how reliably automated evaluators detect them. Rule-based and LLM-Judge evaluators that perform well under Baseline, Direct, and Encouragement show consistent degradation under Socratic feedback. The primary LLM-Judge (Qwen2.5-32B) achieves overall F1 = 0.726 but drops to 0.427 on Socratic items, and a second LLM-Judge (Gemma3-27B) reproduces this degradation with Socratic F1 = 0.342. The four factorial prompt-sensitivity variants reach overall F1 up to 0.844, but their Socratic F1 remains at or below 0.390. These findings show that automated tutoring-feedback auditing must be style-specific, prompt-calibrated, and human-grounded before deployment.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"752-765"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automating Situational Judgment Tests From Likert Scales: A Multiagent LLM Framework for Literacy Assessment 从李克特量表自动化情景判断测试：一个多代理读写能力评估的LLM框架

IF 7.4 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-08-04 DOI: 10.1109/TLT.2026.3720406

Jianwen Sun;Fucheng Guo;Zongkai Yang;Xiaoxuan Shen;Dengkang Chen;Mengqi Wei;Aida Wang

{"title":"Automating Situational Judgment Tests From Likert Scales: A Multiagent LLM Framework for Literacy Assessment","authors":"Jianwen Sun;Fucheng Guo;Zongkai Yang;Xiaoxuan Shen;Dengkang Chen;Mengqi Wei;Aida Wang","doi":"10.1109/TLT.2026.3720406","DOIUrl":"https://doi.org/10.1109/TLT.2026.3720406","url":null,"abstract":"Situational judgment tests (SJTs) are a well-established class of assessment instruments that embed measurement within realistic scenarios, eliciting context-driven responses that are less susceptible to social desirability bias and more effective at capturing applied judgment than traditional self-report scales. Despite these advantages, the widespread adoption of SJTs in educational assessment remains constrained by the high cost and labor intensity of manual item development. To address this scalability bottleneck, we propose Agents for Educational SJTs (AES), a multiagent framework powered by large language models (LLMs) that automatically transforms existing Likert-scale items into psychometrically informed, context-rich SJTs. AES comprises three cooperative modules, generator, simulator, and calibrator, that emulate the expert-driven development pipeline by combining qualitative review from simulated domain experts with quantitative analysis of synthetic student responses, enabling iterative item refinement without human intervention. We instantiate AES in the domain of AI literacy, a complex and affectively rich competency that typifies the constructs for which SJTs are most beneficial. Evaluation on both human participant and LLM-simulated response datasets shows that AES-generated SJT items provide improved psychometric evidence relative to their Likert counterparts, while learners report greater engagement and perceived authenticity. These findings provide initial evidence that AES can serve as a scalable and psychometrically informed approach for supporting the development of scenario-based AI literacy assessments.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"836-848"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MathScrub: Single-Pipeline Strikeout Removal and LaTeX Generation for Handwritten Calculus Expressions 用于手写微积分表达式的单管道剔除和LaTeX生成

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-02-24 DOI: 10.1109/TLT.2026.3667787

Sen Shen;Ning Liu;Lintao Song;Dongkun Han

{"title":"MathScrub: Single-Pipeline Strikeout Removal and LaTeX Generation for Handwritten Calculus Expressions","authors":"Sen Shen;Ning Liu;Lintao Song;Dongkun Han","doi":"10.1109/TLT.2026.3667787","DOIUrl":"https://doi.org/10.1109/TLT.2026.3667787","url":null,"abstract":"With the rapid development of large language models (LLMs), image-to-LaTeX conversion has shown great potential for applications such as automated grading and mathematical expression recognition in educational and assessment settings. However, real-world handwritten Science, Technology, Engineering, and Mathematics (STEM) manuscripts often contain strikeouts, overwrites, and other markings that disrupt handwritten content structure, reducing recognition accuracy and compilability. In this article, we present MathScrub, a single-pipeline multimodal framework that cleans and transcribes handwritten calculus expressions by grouping connected components with geometry-constrained clustering, detecting deletions via an early-fusion vision transformer, applying geometry-aware inpainting, and generating compilable LaTeX with a parameter-efficiently adapted open-source multimodal vision-language model (VLM). To support this task, we introduce Math-Strike, a dataset of handwritten calculus answers with real and synthetic strikeouts, component-level annotations, and aligned LaTeX. Experiments show that MathScrub outperforms open-source baselines on component detection and image-to-LaTeX tasks while remaining robust in real strikeout scenarios, providing a reliable preprocessing stage for LLM-based grading and formula recognition.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"141-154"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11409990","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training 异步演讲训练中多模态情感反馈的可解释闭环智能辅导系统

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-03-15 DOI: 10.1109/TLT.2026.3693864

Hung-Yue Suen;Kuo-En Hung

{"title":"An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training","authors":"Hung-Yue Suen;Kuo-En Hung","doi":"10.1109/TLT.2026.3693864","DOIUrl":"https://doi.org/10.1109/TLT.2026.3693864","url":null,"abstract":"This article presents an interpretable closed-loop Intelligent Tutoring System (ITS) that supports feedback-guided practice for developing on-camera oral presentation skills at scale. The system operationalizes a 7-D Behaviorally Anchored Rating Scale (BARS) and implements a three-layer interpretable feedback architecture that connects rubric-aligned multimodal scoring, audience-perceived expressive diagnostics, and retrieval-augmented conversational coaching to support deliberate practice. Built on an XGBoost backbone, the ITS maps multimodal inputs (facial, vocal, textual, and oculomotor features) into evidence-based feedback that can be traced back to observable performance cues. Trained on 10 360 Massive Open Online Course video segments, the system achieved rubric-aligned scoring with performance levels comparable to expert ratings (R<sup>2</sup> = 0.48–0.61, ρ = 0.69–0.78, MAE = 0.43–0.57). In a pre–post validation study with 204 adult learners over a 30-day practice window, participants demonstrated significant improvements across all seven BARS dimensions (Cohen's d = 0.39–0.90), with practice frequency showing a strong positive association with posttest performance after controlling for baseline scores and demographics. The results demonstrate how multimodal analytic outputs can be systematically transformed into observable behavioral change through an integrated feedback architecture, advancing explainable and pedagogically grounded ITS design for performance-based competencies.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"480-491"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bring the Artificial Intelligence to Tutoring Robots in Education: A Systematic Literature Review 将智能辅导机器人引入教育：系统性文献综述

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2024-08-29 DOI: 10.1109/TLT.2024.3428366

Xinyue Zhang;Fangqing Zhu;Kun Wang;Guitao Cao;Yaofeng Xue;Mingzhuo Liu

{"title":"Bring the Artificial Intelligence to Tutoring Robots in Education: A Systematic Literature Review","authors":"Xinyue Zhang;Fangqing Zhu;Kun Wang;Guitao Cao;Yaofeng Xue;Mingzhuo Liu","doi":"10.1109/TLT.2024.3428366","DOIUrl":"10.1109/TLT.2024.3428366","url":null,"abstract":"Advances in artificial intelligence technologies have given rise to traditional robotics. Intelligent tutoring robots, supported by artificial intelligence technologies and hardware design, have sparked a new revolution in all walks of traditional education. Despite the growing number of research papers published on intelligent tutoring robots, and a few limited reviews focusing on specific technologies or theoretical aspects, there is still no wide-ranging review work that systematically summarizes the studies from the perspectives of theoretical level and practical implications concluded in experiments. To address this challenge and promote a common understanding of the concept of intelligent tutoring robot, we conducted a systematic literature review of papers published between 2016 and 2023. We investigated the construction of intelligent tutoring robots, the advanced artificial intelligence technologies employed, and intelligent tutoring robots’ applications in educational contexts. From a total of 1751 publications, we selected and analyzed 105 main studies to identify and address three research questions: 1) How does the construction of intelligent tutoring robots affect education? 2) What artificial intelligence technologies enable intelligent tutoring robots to be “intelligent”? 3) How are intelligent tutoring robots applied in educational contexts? In addition, we discuss the challenges of current research and provide an outlook on future development trends to provide guidelines for participators and researchers in the fields of intelligent tutoring robots.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"229-248"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond Student Outputs: Design Requirements for Teacher-Facing Process Analytics in AI-Assisted Block-Based Programming 超越学生输出：在人工智能辅助的基于块的编程中面向教师的过程分析的设计要求

IF 7.4 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-07-30 DOI: 10.1109/TLT.2026.3718805

Jaecheon Jeon;Junhyeok Jang;Soon Ki Jung

{"title":"Beyond Student Outputs: Design Requirements for Teacher-Facing Process Analytics in AI-Assisted Block-Based Programming","authors":"Jaecheon Jeon;Junhyeok Jang;Soon Ki Jung","doi":"10.1109/TLT.2026.3718805","DOIUrl":"https://doi.org/10.1109/TLT.2026.3718805","url":null,"abstract":"This article reports an early stage requirement and interpretability study of teacher-facing process analytics for artificial intelligence (AI)-assisted block-based programming. The study elicited teachers’ judgments about what process evidence, report elements, and guardrail conditions would be needed when students may consult external generative AI while solving Entry-based tasks. An online survey of 312 current elementary teachers in Daegu Metropolitan City and Gyeongsangbuk-do, South Korea, was analyzed using descriptive statistics, readability–usefulness mapping, exploratory regression analyses, and inductive thematic analysis. Teachers strongly endorsed the need for process evidence beyond final products. Across all five candidate evidence types, mean perceived instructional usefulness was descriptively higher than mean conceptual readability, indicating that several signals were valued for classroom judgment while requiring interpretive support. Teachers also prioritized one-page summaries, visible evidence, plain-language wording, and brief intervention cues, together with editable interpretations, uncertainty cues, nonlabeling language, and clear privacy rules. The findings provide survey-grounded early stage design requirements for teacher-facing analytics by identifying candidate process-evidence types, capture-to-report translation needs, and guardrail conditions that support evidence-linked, revisable summaries under teacher judgment.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"791-803"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148781014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Cognitive Processes of Learners Based on BiGRU-Attention: A Dual-Phase Framework 基于BiGRU-Attention的学习者认知过程建模：一个双阶段框架

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-04-07 DOI: 10.1109/TLT.2026.3681668

Beibei Liu;Fei Hao;Ruoxia Yao;Yishui Zhu;Liang Zhao

{"title":"Modeling Cognitive Processes of Learners Based on BiGRU-Attention: A Dual-Phase Framework","authors":"Beibei Liu;Fei Hao;Ruoxia Yao;Yishui Zhu;Liang Zhao","doi":"10.1109/TLT.2026.3681668","DOIUrl":"https://doi.org/10.1109/TLT.2026.3681668","url":null,"abstract":"Cognitive diagnosis (CD) and knowledge tracing (KT) are two core tasks for assessing learner states in intelligent educational systems. Although joint modeling studies have attempted to incorporate diagnostic information into the KT process, most approaches employ loosely coupled designs or rely on implicit utilization of cognitive cues, making it challenging to establish a principled, structured connection between static cognitive assessment and dynamic sequence prediction. This article proposes a dual-phase cognitive processes modeling framework (DPF) that explicitly connects CD and KT through a cognitive prior constraint mechanism. In Phase I, the model fuses multidimensional interactive data, including emotional features, to infer learners’ cognitive states, yielding continuous and interpretable cognitive representations. In Phase II, these representations initialize the hidden states of the sequence model, imposing cognitive constraints on the sequential modeling of learners’ response behaviors to guide response correctness prediction on specific knowledge components. Experiments conducted on multiple real-world educational datasets show that the DPF achieves competitive performance compared with classical CD models and representative KT methods in both diagnostic capability and prediction accuracy. Ablation studies further indicate that the observed improvements are mainly related to the explicit incorporation of cognitive prior constraints rather than solely increased model complexity. Overall, this study proposes a cognition-guided modeling paradigm that effectively bridges static cognitive assessment and dynamic learning prediction within a unified framework.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"316-329"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0