Search for: All records

As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students’ online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. 

Studies have proven that providing on-demand assistance, additional instruction on a problem when a student requests it, improves student learning in online learning environments. Additionally, crowdsourced, on-demand assistance generated from educators in the field is also effective. However, when provided on-demand assistance in these studies, students received assistance using problem-based randomization, where each condition represents a different assistance, for every problem encountered. As such, claims about a given educator’s effectiveness are provided on a per-assistance basis and not easily generalizable across all students and problems. This work aims to provide stronger claims on which educators are the most effective at generating on-demand assistance. Students will receive on-demand assistance using educator-based randomization, where each condition represents a different educator who has generated a piece of assistance, allowing students to be kept in the same condition over longer periods of time. Furthermore, this work also attempts to find additional benefits to providing students assistance generated by the same educator compared to a random assistance available for the given problem. All data and analysis being conducted can be found on the Open Science Foundation website 

To improve student learning outcomes within online learning platforms, struggling students are often provided with on-demand supplemental instructional content. Recently, services like Yup (yup.com) and UPcheive (upchieve.org) have begun to offer on-demand live tutoring sessions with qualified educators, but the availability of tutors and the cost associated with hiring them prevents many students from having access to live support. To help struggling students and offset the inequities intrinsic to high-cost services, we are attempting to develop a process that uses large language representation models to algorithmically identify relevant support messages from these chat logs, and distribute them to all students struggling with the same content. In an empirical evaluation of our methodology we were able to identify messages from tutors to students struggling with middle school mathematics problems that qualified as explanations of the content. However, when we distributed these explanations to students outside of the tutoring sessions, they had an overall negative effect on the students’ learning. Moving forward, we want to be able to identify messages that will promote equity and have a positive impact on students. 

Studies have shown that on-demand assistance, additional instruction given on a problem per student request, improves student learning in online learning environments. Students may have opinions on whether an assistance was effective at improving student learning. As students are the driving force behind the effectiveness of assistance, there could exist a correlation between students’ perceptions of effectiveness and the computed effectiveness of the assistance. This work conducts a survey asking secondary education students on whether a given assistance is effective in solving a problem in an online learning platform. It then provides a cursory glance at the data to view whether a correlation exists between student perception and the measured effectiveness of an assistance. Over a three year period, approximately twenty-two thousand responses were collected across nearly four thousand, four hundred students. Initial analyses of the survey suggest no significance in the relationship between student perception and computed effectiveness of an assistance, regardless of if the student participated in the survey. All data and analysis conducted can be found on the Open Science Foundation website. 

Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn. One can use process data for many downstream tasks such as learning outcome prediction and automatically delivering personalized intervention. In this paper, we propose a framework for learning representations of educational process data that is applicable across different learning scenarios. Our framework consists of a pre-training step that uses BERTtype objectives to learn representations from sequential process data and a fine-tuning step that further adjusts these representations on downstream prediction tasks. We apply our framework to the 2019 nation’s report card data mining competition dataset that consists of student problem-solving process data and detail the specific models we use in this scenario. We conduct both quantitative and qualitative experiments to show that our framework results in process data representations that are both predictive and informative. 

This paper drills deeper into the documented effects of the Cognitive Tutor Algebra I and ASSISTments intelligent tutoring systems by estimating their effects on specific problems. We start by describing a multilevel Rasch-type model that facilitates testing for differences in the effects between problems and precise problem-specific effect estimation without the need for multiple comparisons corrections. We find that the effects of both intelligent tutors vary between problems– the effects are positive for some, negative for others, and undeterminable for the rest. Next we explore hypotheses explaining why effects might be larger for some problems than for others. In the case of ASSISTments, there is no evidence that problems that are more closely related to students’ work in the tutor displayed larger treatment effects. 

Open-ended questions in mathematics are commonly used by teachers to monitor and assess students’ deeper conceptual understanding of content. Student answers to these types of questions often exhibit a combination of language, drawn diagrams and tables, and mathematical formulas and expressions that supply teachers with insight into the processes and strategies adopted by students in formulating their responses. While these student responses help to inform teachers on their students’ progress and understanding, the amount of variation in these responses can make it difficult and time-consuming for teachers to manually read, assess, and provide feedback to student work. For this reason, there has been a growing body of research in developing AI-powered tools to support teachers in this task. This work seeks to build upon this prior research by introducing a model that is designed to help automate the assessment of student responses to open-ended questions in mathematics through sentence-level semantic representations. We find that this model outperforms previously published benchmarks across three different metrics. With this model, we conduct an error analysis to examine characteristics of student responses that may be considered to further improve the method.