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The educational data mining community has extensively investigated affect detection in learning platforms, finding associations between affective states and a wide range of learning outcomes. Based on these insights, several studies have used affect detectors to create interventions tailored to respond to when students are bored, confused, or frustrated. However, these detector-based interventions have depended on detecting affect when it occurs and therefore inherently respond to affective states after they have begun. This might not always be soon enough to avoid a negative experience for the student. In this paper, we aim to predict students' affective states in advance. Within our approach, we attempt to determine the maximum prediction window where detector performance remains sufficiently high, documenting the decay in performance when this prediction horizon is increased. Our results indicate that it is possible to predict confusion, frustration, and boredom in advance with performance over chance for prediction horizons of 120, 40, and 50 seconds, respectively. These findings open the door to designing more timely interventions. 

Many online learning platforms and MOOCs incorporate some amount of video-based content into their platform, but there are few randomized controlled experiments that evaluate the effectiveness of the different methods of video integration. Given the large amount of publicly available educational videos, an investigation into this content's impact on students could help lead to more effective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learning platform that allowed students to request skill-related videos while completing their online middle-school mathematics assignments. A total of 18,535 students participated in two large-scale randomized controlled experiments related to providing students with publicly available educational videos. The first experiment investigated the effect of providing students with the opportunity to request these videos, and the second experiment investigated the effect of using a multi-armed bandit algorithm to recommend relevant videos. Additionally, this work investigated which features of the videos were significantly predictive of students' performance and which features could be used to personalize students' learning. Ultimately, students were mostly disinterested in the skill-related videos, preferring instead to use the platforms existing problem-specific support, and there was no statistically significant findings in either experiment. Additionally, while no video features were significantly predictive of students' performance, two video features had significant qualitative interactions with students' prior knowledge, which showed that different content creators were more effective for different groups of students. These findings can be used to inform the design of future video-based features within online learning platforms and the creation of different educational videos specifically targeting higher or lower knowledge students. The data and code used in this work can be found at https://osf.io/cxkzf/. 

Teachers often rely on the use of a range of open-ended problems to assess students’ understanding of mathematical concepts. Beyond traditional conceptions of student openended work, commonly in the form of textual short-answer or essay responses, the use of figures, tables, number lines, graphs, and pictographs are other examples of open-ended work common in mathematics. While recent developments in areas of natural language processing and machine learning have led to automated methods to score student open-ended work, these methods have largely been limited to textual answers. Several computer-based learning systems allow students to take pictures of hand-written work and include such images within their answers to open-ended questions. With that, however, there are few-to-no existing solutions that support the auto-scoring of student hand-written or drawn answers to questions. In this work, we build upon an existing method for auto-scoring textual student answers and explore the use of OpenAI/CLIP, a deep learning embedding method designed to represent both images and text, as well as Optical Character Recognition (OCR) to improve model performance. We evaluate the performance of our method on a dataset of student open-responses that contains both text- and image-based responses, and find a reduction of model error in the presence of images when controlling for other answer-level features. 

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to ”detector rot.” We compare the generalizability of Knowledge Training (KT) models by comparing model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curricula available through Open Educational Resources. We observed that the models generally were highly performant in predicting student learning within an academic year, whereas certain academic years were more generalizable than other academic years. We posit that the Knowledge Tracing models are relatively stable in terms of performance across academic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evidence in this paper, we posit that learning platforms leveraging KT models need to be mindful of systemic changes or drastic changes in certain user demographics. 

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.