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  1. Rodrigo, M.M. ; Matsuda, N. ; Cristea, A.I. ; Dimitrova, V. (Ed.)
    This paper presents the design and evaluation of an automated writing evaluation system that integrates natural language processing (NLP) and user interface design to support students in an important writing skill, namely, self-monitored revising. Results from a classroom deployment suggest that NLP can accurately analyze where and what kind of revisions students make across paper drafts, that students engage in self-monitored revising, and that the interfaces for visualizing the NLP results are perceived by students to be useful.
    Free, publicly-accessible full text available July 1, 2023
  2. Rodrigo, M.M. ; Matsuda, N. ; Cristea, A.I. ; Dimitrova, V. (Ed.)
    This paper presents the design and evaluation of an automated writing evaluation system that integrates natural language processing (NLP) and user interface design to support students in an important writing skill, namely, self-monitored revising. Results from a classroom deployment suggest that NLP can accurately analyze where and what kind of revisions students make across paper drafts, that students engage in self-monitored revising, and that the interfaces for visualizing the NLP results are perceived by students to be useful.
    Free, publicly-accessible full text available July 1, 2023
  3. Rodrigo, M.M. ; Matsuda, N. ; Cristea, A.I. ; Dimitrova, V. (Ed.)
    It might be highly effective if students could transition dynamically between individual and collaborative learning activities, but how could teachers manage such complex classroom scenarios? Although recent work in AIED has focused on teacher tools, little is known about how to orchestrate dynamic transitions between individual and collaborative learning. We created a novel technology ecosystem that supports these dynamic transitions. The ecosystem integrates a novel teacher orchestration tool that provides monitoring support and pairing suggestions with two AI-based tutoring systems that support individual and collaborative learning, respectively. We tested the feasibility of this ecosystem in a classroom study with 5 teachers and 199 students over 22 class sessions. We found that the teachers were able to manage the dynamic transitions and valued them. The study contributes a new technology ecosystem for dynamically transitioning between individual and collaborative learning, plus insight into the orchestration functionality that makes these transitions feasible.
    Free, publicly-accessible full text available January 1, 2023
  4. Roll, I ; McNamara, D ; Sosnovsky, S ; Luckin, R ; Dimitrova, V. (Ed.)
    Knowledge tracing refers to a family of methods that estimate each student’s knowledge component/skill mastery level from their past responses to questions. One key limitation of most existing knowledge tracing methods is that they can only estimate an overall knowledge level of a student per knowledge component/skill since they analyze only the (usually binary-valued) correctness of student responses. Therefore, it is hard to use them to diagnose specific student errors. In this paper, we extend existing knowledge tracing methods beyond correctness prediction to the task of predicting the exact option students select in multiple choice questions. We quantitatively evaluate the performance of our option tracing methods on two large-scale student response datasets. We also qualitatively evaluate their ability in identifying common student errors in the form of clusters of incorrect options across different questions that correspond to the same error.
  5. Roll, I. ; McNamara, D. ; Sosnovsky, S. ; Luckin, R. ; Dimitrova, V. (Ed.)
    Scaffolding and providing feedback on problem-solving activities during online learning has consistently been shown to improve performance in younger learners. However, less is known about the impacts of feedback strategies on adult learners. This paper investigates how two computer-based support strategies, hints and required scaffolding questions, contribute to performance and behavior in an edX MOOC with integrated assignments from ASSISTments, a web-based platform that implements diverse student supports. Results from a sample of 188 adult learners indicated that those given scaffolds benefited less from ASSISTments support and were more likely to request the correct answer from the system.