![]() ![]() The ability to predict such outcomes enables tutoring systems to adjust interventions and ultimately yield improved student learning. We propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS) by analyzing their faces and gestures. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 20. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice-one that groups students randomly and one that groups students based on a unidimensional course score. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. Furthermore, the role of the three branches of AI (i.e., natural language processing, educational data mining, and learning analytics) in feedback practices and potential areas for their future development are discussed.Ĭomputer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. This paper aims to inform both researchers and practitioners about the present and future of AI applications in feedback practices, identify and organize potential areas for the use of AI for feedback purposes, and establish venues for AI research and practice in educational feedback. ![]() Despite the rapid uptake of digital technologies in education, previous studies on educational feedback primarily focused on the theoretical underpinnings of feedback practices, which are limited in terms of their coverage of AI-based technologies. Advanced technologies powered by Artificial Intelligence (AI) enable teachers to generate different types of feedback supporting student learning. As advancements in technology have enabled the adoption of digital learning environments with assessment capabilities, the frequency, delivery format, and timeliness of feedback derived from educational assessments have also changed progressively. Intelligent tutoring effects in these evaluations were small, suggesting that evaluation results are also affected by the nature of control treatments and the adequacy of program implementations.įeedback is a crucial component of student learning. The review also describes findings from two groups of evaluations that did not meet all of the selection requirements for the meta-analysis: six evaluations with nonconventional control groups and four with flawed implementations of intelligent tutoring systems. However, the amount of improvement found in an evaluation depended to a great extent on whether improvement was measured on locally developed or standardized tests, suggesting that alignment of test and instructional objectives is a critical determinant of evaluation results. The median effect of intelligent tutoring in the 50 evaluations was to raise test scores 0.66 standard deviations over conventional levels, or from the 50th to the 75th percentile. This review describes a meta-analysis of findings from 50 controlled evaluations of intelligent computer tutoring systems. ![]()
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