Writing is an iterative process, yet traditional assessments often prioritize the final product over the transformations that shape it. This study investigates the complex nature of revision in student writing through computational modeling, leveraging keystroke data to capture and analyze revision behaviors. Focusing on a dataset of 1,975 annotated revision events from 10th-grade French students, we assess multiple methodological approaches, including rule-based heuristics, machine learning classifiers, and large language models (LLMs). While previous research has demonstrated the feasibility of automated revision detection, we extend these efforts by introducing a novel framework for identifying embedded revisions, i.e. instances where a revision occurs within another. By comparing the efficacy of different computational strategies, our findings reveal key insights into how revisions unfold in realtime writing. Annotations evaluated by agreement measures underline the complexity of the task. This work not only enhances the precision of automated revision classification but also lays the groundwork for intelligent writing support systems that provide targeted feedback to students, fostering a deeper engagement with the revision process.
Adaptive learning technologies observe learner performance, infer mastery, and dynamically tailor instruction. However, this approach can fall short when encountering a learning phenomenon we term “deceptive overgeneralization”, where learners perform correct actions based on incomplete understanding. This phenomenon “deceives” adaptive systems into prematurely stopping necessary practice, leaving overgeneralization unaddressed. To address this, we empirically investigated the mechanisms, risks, and remediation strategies for deceptive overgeneralization through experiments using Intelligent Tutoring Systems (ITSs) for Riichi Mahjong. Experiment 1 provided evidence supporting the effectiveness of ITSs in teaching Riichi Mahjong, showing a large effect size. However, despite their overall effectiveness, Experiment 2 revealed that ITSs may fall short in cases of deceptive overgeneralization, as learners, after demonstrating seemingly satisfactory performance during initial practice, subsequently misapplied learned actions “confidently” in scenarios that did not warrant them. Experiments 3–4 replicated this finding and further revealed that adaptive learning systems relying on observed correctness can prematurely cease practice, leaving deceptive overgeneralization unaddressed. Experiments 5–6 replicated the findings of Experiments 3–4, and also extended the methodology by incorporating tailored practice designed via our systematic detection/remediation procedure, which successfully addressed deceptive overgeneralization. This study contributes both theoretically and practically to enhancing the precision of adaptive learning, enabling more accurate mastery assessments and improved learning outcomes.
Artificial Intelligence Educational Tools (AIED) are poised to transform educational practices by enabling personalised learning, automating administrative tasks, and delivering real-time feedback. However, the ethical and constructive implementation of AIED in K-12 education remains a pressing concern, particularly among teachers. Even when teachers view AIED as a highly trans formative tool, they often remain reluctant to experiment with it in the classroom, primarily because they feel unprepared and lack confidence in using AIED effectively. This study aims to address these gaps by exploring the constructive applications of AIED to empower teachers in enhancing students' cognitive engagement (based on ICAP framework) and higher-order thinking skills (HOTS). A five -month teacher training course was conducted, incorporating pre- and postsurveys to assess teachers' confidence, knowledge, and appropriation of AIED. Teachers engaged in training on AI-related risks and tools, designed lessons that integrated AIED, and gathered student feedback. Teacher training surveys (N = 17) were analysed with paired samples T-tests; deductive content analysis focusing on HOTS and the role of AIED was conducted based on ten lesson plans. For triangulation, descriptive analysis was applied to the student feedback survey (N = 240) about their perception of such learning activities. Overall, the study shows that teacher training empowered teachers in using AIED; the alignment of lesson activities with ICAP principles might depend on the learning community; and AI-supported learning can foster HOTS depending on the type of learning activities and the AIED role.
With the increasing presence of technology in education, foundational skills that can encourage learners' understanding of and meaningful interactions with technology should not be overlooked. Computational thinking (CT) is one such skill with applications to multiple domains. While CT assessment has traditionally focused on final outcomes, a more comprehensive perspective can emerge by also examining the process through means such as log data. The aims of this scoping review are to map how existing research has utilized log data to assess CT and to present research gaps for future studies in this area. Multiple databases from different disciplines were searched for peer-reviewed journal articles and conference papers. Relevant studies analyzed log data of individual learners from open-ended tasks to gauge CT performance. The following five areas were examined from each study: 1) form of CT, 2) education level, 3) log data process features, 4) interpretations of the features, and 5) theoretical contextualization of the features. The analysis revealed text-based programming as the most researched form of CT, followed by block-based programming Additionally, higher education was the most studied level of education. Regarding log data features, attempts and time on task were among the most extracted. In studies justifying the choice of features, a variety of theories and empirical studies were referenced. The suggested interpretation of the features also varied, leading to inconsistent conclusions about what could be inferred from the log data. This valuable insight into existing research patterns can steer future studies towards theory -based measures in underrepresented areas of research, advancing the discussion on how technology can enhance learning. Furthermore, using log data for assessment can enable scalable personalized feedback and scaffolding. Combining a strong theoretical understanding of these topics from educational sciences with the technology-oriented approach to log data could substantially promote new ways of developing effective interventions.
Generative AI (genAI) tools are increasingly being integrated into instructional design workflows for content creation, assessment development, and lesson planning. For novice designers, it is critical to understand whether this integration supports the design process without compromising underlying pedagogical learning. This study addresses this gap through an ecologically valid field experiment within a 14-week graduate course training novice instructional designers. Using a counterbalanced A/B design embedded in authentic coursework, students created eight microlessons, alternating genAI assistance with independent work. Learning of pedagogical principles was assessed via module pre/post-tests and self-efficacy, in teaching practices and genAI use, was measured via course-level pre/post-surveys. Results showed no evidence that genAI use hindered learning, as post-test scores on module content remained stable or improved, despite variations in test form difficulty. Students demonstrated substantial and statistically significant gains by over 10
