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Gaming the system is a persistent problem in Computer-Based Learning Platforms. While substantialprogress has been made in identifying and understanding such behaviors, effective interventions remainscarce. This study uses a method of causal moderation known as Fully Latent Principal Stratification toexplore the impact of two types of interventions – gamification and manipulation of assistance access –on the learning outcomes of students who tend to game the system. The results indicate that gamificationdoes not consistently mitigate these negative behaviors. One gamified condition had a consistentlypositive effect on learning regardless of students’ propensity to game the system, whereas the other had anegative effect on gamers. However, delaying access to hints and feedback may have a positive effect onthe learning outcomes of those gaming the system. This paper also illustrates the potential for integratingdetection and causal methodologies within educational data mining to evaluate effective responses to detectedbehaviors. 

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 open- ended 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 an- swers. Several computer-based learning systems allow stu- dents 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 ex- isting 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. 

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 effective- ness 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 ef- fective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learn- ing platform that allowed students to request skill-related videos while completing their online middle-school mathematics assign- ments. 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 fea- tures 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 is hosted by the Open Science Foundation. 

Large language models have recently been able to perform well in a wide variety of circumstances. In this work, we explore the possi- bility of large language models, specifically GPT-3, to write explanations for middle-school mathematics problems, with the goal of eventually us- ing this process to rapidly generate explanations for the mathematics problems of new curricula as they emerge, shortening the time to inte- grate new curricula into online learning platforms. To generate expla- nations, two approaches were taken. The first approach attempted to summarize the salient advice in tutoring chat logs between students and live tutors. The second approach attempted to generate explanations us- ing few-shot learning from explanations written by teachers for similar mathematics problems. After explanations were generated, a survey was used to compare their quality to that of explanations written by teachers. We test our methodology using the GPT-3 language model. Ultimately, the synthetic explanations were unable to outperform teacher written explanations. In the future more powerful large language models may be employed, and GPT-3 may still be effective as a tool to augment teach- ers’ process for writing explanations, rather than as a tool to replace them. The explanations, survey results, analysis code, and a dataset of tutoring chat logs are all available at https://osf.io/wh5n9/. 

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in math- ematics, is a well-established and proven approach in learn- ing analytics. In this work, we report on our analysis exam- ining the generalizability of BKT models across academic years attributed to ”detector rot.” We compare the gen- eralizability of Knowledge Training (KT) models by com- paring model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curric- ula available through Open Educational Resources. We ob- served 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 mod- els are relatively stable in terms of performance across aca- demic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evi- dence in this paper, we posit that learning platforms lever- aging KT models need to be mindful of systemic changes or drastic changes in certain user demographics. 

Feedback is a crucial factor in mathematics learning and in- struction. Whether expressed as indicators of correctness or textual com- ments, feedback can help guide students’ understanding of content. Be- yond this, however, teacher-written messages and comments can provide motivational and affective benefits for students. The question emerges as to what constitutes effective feedback to promote not only student learning but also motivation and engagement. Teachers may have differ- ent perceptions of what constitutes effective feedback utilizing different tones in their writing to communicate their sentiment while assessing student work. This study aims to investigate trends in teacher senti- ment and tone when providing feedback to students in a middle school mathematics class context. Toward this, we examine the applicability of state-of-the-art sentiment analysis methods in a mathematics context and explore the use of punctuation marks in teacher feedback messages as a measure of tone. 

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in math- ematics, is a well-established and proven approach in learn- ing analytics. In this work, we report on our analysis exam- ining the generalizability of BKT models across academic years attributed to ”detector rot.” We compare the gen- eralizability of Knowledge Training (KT) models by com- paring model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curric- ula available through Open Educational Resources. We ob- served 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 mod- els are relatively stable in terms of performance across aca- demic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evi- dence in this paper, we posit that learning platforms lever- aging KT models need to be mindful of systemic changes or drastic changes in certain user demographics. 

Math performance continues to be an important focus for improve- ment. Many districts adopted educational technology programs to support student learning and teacher instruction. The ASSISTments program provides feedback to students as they solve homework problems and automatically prepares reports for teachers about student performance on daily assignments. During the 2018- 19 and 2019-20 school years, WestEd led a large-scale randomized controlled trial to replicate the effects of ASSISTments in 63 schools in North Carolina in the US. 32 treatment schools implemented ASSISTments in 7th-grade math class- rooms. Recently, we conducted a follow-up analysis to measure the long-term effects of ASSISTments on student performance one year after the intervention, when the students were in 8th grade. The initial results suggested that implement- ing ASSISTments in 7th grade improved students’ performance in 8th grade and minority students benefited more from the intervention.