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.

In this study, we present an approach to utilizing variance in students’ performance across different formats (multiple-choice, numeric input, word problems) as a target for personalization. We have developed a measure called challenge variance, that indicates the degree to which different formats pose varying levels of challenge for individual learners. We investigated whether challenge variance could be a useful source of information for developing learner models by analyzing data from an online math tutoring platform. Results demonstrated that challenge variance has a relationship with an external activity, indicating its utility as a means of predicting how well a learner will perform in a new setting. We discuss the affordances and issues with the measure and whether or not it could be a useful additional tool in developing personalized learner models as an intuitive and platform-agnostic measure of performance.

Because Chinese reading and writing systems are not phonetic, Mandarin Chinese learners must construct six-way mental connections in order to learn new words, linking characters, meanings, and sounds. Little research has focused on the difficulties inherent to each specific component involved in this process, especially within digital learning environments. The present work examines Chinese word acquisition within ASSISTments, an online learning platform traditionally known for mathematics education. Students were randomly assigned to one of three conditions in which researchers manipulated a learning assignment to exclude one of three bi-directional connections thought to be required for Chinese language acquisition (i.e., sound-meaning and meaning-sound). Researchers then examined whether students’ performance differed significantly when the learning assignment lacked sound-character, character-meaning, or meaning-sound connection pairs, and whether certain problem types were more difficult for students than others. Assessment of problems by component type (i.e., characters, meanings, and sounds) revealed support for the relative ease of problems that provided sounds, with students exhibiting higher accuracy with fewer attempts and less need for system feedback when sounds were included. However, analysis revealed no significant differences in word acquisition by condition, as evidenced by next-day post-test scores or pre-to post-test gain scores. Implications and suggestions for future work are discussed.

Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system designed to provide researchers with insights regarding data logged from randomized controlled experiments conducted within the ASSISTments TestBed. The Assessment of Learning Infrastructure (ALI) builds upon existing technologies to foster a symbiotic relationship beneficial to students, researchers, the platform and its content, and the learning analytics community. ALI is a sophisticated automated reporting system that provides an overview of sample distributions and basic analyses for researchers to consider when assessing their data. ALI's benefits can also be felt at scale through analyses that crosscut multiple studies to drive iterative platform improvements while promoting personalized learning.

The focus of the learning analytics community bridges the gap between controlled educational research and data mining. Online learning platforms can be used to conduct randomized controlled trials to assist in the development of interventions that increase learning gains; datasets from such research can act as a treasure trove for inquisitive data miners. The present work employs a data mining approach on randomized controlled trial data from ASSISTments, a popular online learning platform, to assess the benefits of incorporating additional student performance data when attempting to differentiate between two user groups. Through a resampling technique, we show that partial credit, defined as an algorithmic combination of binary correctness, hint usage, and attempt count, can benefit assessment and group differentiation. Partial credit reduces sample sizes required to reliably differentiate between groups that are known to differ by 58%, and reduces sample sizes required to reliably differentiate between less distinct groups by 9%.

An interactive demonstration on how to design and implement randomized controlled experiments at scale within the ASSISTments TestBed, a new collaborative for educational research funded by the National Science Foundation (NSF). The Assessment of Learning infrastructure (ALI), a unique data retrieval and analysis tool, is also demonstrated.

Detailed performance data can be exploited to achieve stronger student models when predicting next problem correctness (NPC) within intelligent tutoring systems. However, the availability and importance of these details may differ significantly when considering opportunity count (OC), or the compounded sequence of problems a student experiences within a skill. Inspired by this intuition, the present study introduces the Opportunity Count Model (OCM), a unique approach to student modeling in which separate models are built for differing OCs rather than creating a blanket model that encompasses all OCs. We use Random Forest (RF), which can be used to indicate feature importance, to construct the OCM by considering detailed performance data within tutor log files. Results suggest that OC is significant when modeling student performance and that detailed performance data varies across OCs.

Student modeling within intelligent tutoring systems is a task largely driven by binary models that predict student knowledge or next problem correctness (i.e., Knowledge Tracing (KT)). However, using a binary construct for student assessment often causes researchers to overlook the feedback innate to these platforms. The present study considers a novel method of tabling an algorithmically determined partial credit score and problem difficulty bin for each student's current problem to predict both binary and partial next problem correctness. This study was conducted using log files from ASSISTments, an adaptive mathematics tutor, from the 2012-2013 school year. The dataset consisted of 338,297 problem logs linked to 15,253 unique student identification numbers. Findings suggest that an efficiently tabled model considering partial credit and problem difficulty performs about as well as KT on binary predictions of next problem correctness. This method provides the groundwork for modifying KT in an attempt to optimize student modeling.

In contrast to typical laboratory experiments, the everyday use of online educational resources by large populations and the prevalence of software infrastructure for A/B testing leads us to consider how platforms can embed in vivo experiments that do not merely support research, but ensure practical improvements to their educational components. Examples are presented of randomized experimental comparisons conducted by subsets of the authors in three widely used online educational platforms -- Khan Academy, edX, and ASSISTments. We suggest design principles for platform technology to support randomized experiments that lead to practical improvements -- enabling Iterative Improvement and Collaborative Work -- and explain the benefit of their implementation by WPI co-authors in the ASSISTments platform.