Learning experience designers consistently balance the trade-off between open and close-ended activities. The growth and scalability of Computer Based Learning Platforms (CBLPs) have only magnified the importance of these design trade-offs. CBLPs often utilize close-ended activities (i.e. Multiple-Choice Questions [MCQs]) due to feasibility constraints associated with the use of open-ended activities. MCQs offer certain affordances, such as immediate grading and the use of distractors, setting them apart from open-ended activities. Our current study examines the effectiveness of Fill-In problems as an alternative to MCQs for middle school mathematics. We report on a randomized study conducted from 2017 to 2022, with a total of 6,768 students from middle schools across the US. We observe that, on average, Fill-In problems lead to better post-test performance than MCQs; albeit deeper explorations indicate differences between the two design paradigms to be more nuanced. We find evidence that students with higher math knowledge benefit more from Fill-In problems than those with lower math knowledge.

Gaming the system, characterized by attempting to progress through a learning activity without engaging in essential learning behaviors, remains a persistent problem in computer-based learning platforms. This paper examines a simple intervention to mitigate the harmful effects of gaming the system by evaluating the impact of immediate feedback on students prone to gaming the system. Using a randomized controlled trial comparing two conditions - one with immediate hints and feedback and another with delayed access to such resources - this study employs a Fully Latent Principal Stratification model to determine whether students inclined to game the system would benefit more from the delayed hints and feedback. The results suggest differential effects on learning, indicating that students prone to gaming the system may benefit from restricted or delayed access to on-demand support. However, removing immediate hints and feedback did not fully alleviate the learning disadvantage associated with gaming the system. Additionally, this paper highlights the utility of combining detection methods and causal models to comprehend and effectively respond to students’ behaviors. Overall, these findings contribute to our understanding of effective intervention design that addresses gaming the system behaviors, consequently enhancing learning outcomes in computer-based learning platforms.

Solving mathematical problems is cognitively complex, involving strategy formulation, solution development, and the application of learned concepts. However, gaps in students' knowledge or weakly grasped concepts can lead to errors. Teachers play a crucial role in predicting and addressing these difficulties, which directly influence learning outcomes. However, preemptively identifying misconceptions leading to errors can be challenging. This study leverages historical data to assist teachers in recognizing common errors and addressing gaps in knowledge through feedback. We present a longitudinal analysis of incorrect answers from the 2015-2020 academic years on two curricula, Illustrative Math and EngageNY, for grades 6, 7, and 8. We find consistent errors across 5 years despite varying student and teacher populations. Based on these Common Wrong Answers (CWAs), we designed a crowdsourcing platform for teachers to provide Common Wrong Answer Feedback (CWAF). This paper reports on an in vivo randomized study testing the effectiveness of CWAFs in two scenarios: next-problem-correctness within-skill and next-problem-correctness within-assignment, regardless of the skill. We find that receiving CWAF leads to a significant increase in correctness for consecutive problems within-skill. However, the effect was not significant for all consecutive problems within-assignment, irrespective of the associated skill. This paper investigates the potential of scalable approaches in identifying Common Wrong Answers (CWAs) and how the use of crowdsourced CWAFs can enhance student learning through remediation.

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/.

Computer-assisted learning platforms (CALPS) increasingly include gamified elements to improve student outcomes by enhancing their engagement with content. Although evidence exists that gamified programs increase engagement and learning outcomes, there is little causal research on what programmatic mechanisms drive the effect between engagement and learning. In the following paper, we explore this relationship through a method of causal moderation known as fully latent principal stratification. Using data from a large-scale randomized control trial assessing gamified and traditional CALP systems' effects on algebraic knowledge, we estimate the impact of using the gamified CALP on students who engage with one of its key gamification elements---replaying a problem after a suboptimal attempt. The gamified CALP asks students to manipulate algebraic expressions from start to goal states and provides feedback based on the efficiency of these manipulations, allowing students to replay the problems when their efficiency can be improved. We find that the effect of gamification is greater for students with a higher propensity to replay problems. This finding suggests that gamification elements that provide students with opportunities to retry problems are driving the game's efficacy and provide evidence for a scalable mechanism of gamification that can improve students' learning.

This work proposes Dynamic Linear Epsilon-Greedy, a novel contextual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning approaches have trade-offs between empirical investigation and maximal impact on users. Our algorithm seeks to balance these objectives, allowing platforms to personalize content effectively while still gathering valuable data. Dynamic Linear Epsilon-Greedy was evaluated via simulation and an empirical study in the ASSISTments online learning platform. In simulation, Dynamic Linear Epsilon-Greedy performed comparably to existing algorithms and in ASSISTments, slightly increased students’ learning compared to A/B testing. Data collected from its recommendations allowed for the identification of qualitative interactions, which showed high and low knowledge students benefited from different content. Dynamic Linear Epsilon-Greedy holds promise as a method to balance personalization with unbiased statistical analysis. All the data collected during the simulation and empirical study are publicly available at https://osf.io/zuwf7/.

Personalized learning stems from the idea that students benefit from instructional material tailored to their needs. Many online learning platforms purport to implement some form of personalized learning, often through on-demand tutoring or self-paced instruction, but to our knowledge none have a way to automatically explore for specific opportunities to personalize students' education nor a transparent way to identify the effects of personalization on specific groups of students. In this work we present the Automatic Personalized Learning Service (APLS). The APLS uses multi-armed bandit algorithms to recommend the most effective support to each student that requests assistance when completing their online work, and is currently used by ASSISTments, an online learning platform. The first empirical study of the APLS found that Beta-Bernoulli Thompson Sampling, a popular and effective multi-armed bandit algorithm, was only slightly more capable of selecting helpful support than randomly selecting from the relevant support options. Therefore, we also present Decision Tree Thompson Sampling (DTTS), a novel contextual multi-armed bandit algorithm that integrates the transparency and interpretability of decision trees into Thomson sampling. In simulation, DTTS overcame the challenges of recommending support within an online learning platform and was able to increase students' learning by as much as 10% more than the current algorithm used by the APLS. We demonstrate that DTTS is able to identify qualitative interactions that not only help determine the most effective support for students, but that also generalize well to new students, problems, and support content. The APLS using DTTS is now being deployed at scale within ASSISTments and is a promising tool for all educational learning platforms.

As more educators integrate their curricula with online learning, it is easier to crowdsource content from them. Crowdsourced tutoring has been proven to reliably increase students' next problem correctness. In this work, we confirmed the findings of a previous study in this area, with stronger confidence margins than previously, and revealed that only a portion of crowdsourced content creators had a reliable benefit to students. Furthermore, this work provides a method to rank content creators relative to each other, which was used to determine which content creators were most effective overall, and which content creators were most effective for specific groups of students. When exploring data from TeacherASSIST, a feature within the ASSISTments learning platform that crowdsources tutoring from teachers, we found that while overall this program provides a benefit to students, some teachers created more effective content than others. Despite this finding, we did not find evidence that the effectiveness of content reliably varied by student knowledge-level, suggesting that the content is unlikely suitable for personalizing instruction based on student knowledge alone. These findings are promising for the future of crowdsourced tutoring as they help provide a foundation for assessing the quality of crowdsourced content and investigating content for opportunities to personalize students' education.