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1. Generalizing Predictive Models of Reading Ability in Adaptive Mathematics Software

   Almoubayyed, Husni; Fancsali, Stephen; Ritter, Steve (January 2023, Proceedings of the 16th International Conference on Educational Data Mining)

   Feng, Mingyu; Käser, Tanja; Talukdar, Partha (Ed.)

   Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students’ use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond performance in the target domain for instruction. We investigate the extent to which generalization is possible for a recently developed predictive model that seeks to infer students’ reading comprehension ability (as measured by end-of-year standardized test scores) using an introductory learning experience in Carnegie Learning’s MATHia intelligent tutoring system for mathematics. Building on a model learned on data from middle school students in a single school district in a mid-western U.S. state, using that state’s end-of-year English Language Arts (ELA) standardized test score as an outcome, we consider data from a school district in a south-eastern U.S. state as well as that state’s end-of-year ELA standardized test outcome. Generalization is explored by considering prediction performance when training and testing models on data from each of the individual school districts (and for their respective state’s test outcomes) as well as pooling data from both districts together. We conclude with discussion of investigations of some algorithmic fairness characteristics of the learned models. The results suggest that a model trained on data from the smaller of the two school districts considered may achieve greater fairness in its predictions over models trained on data from the other district or both districts, despite broad, overall similarities in some demographic characteristics of the two school districts. This raises interesting questions for future research on generalizing these kinds of models as well as on ensuring algorithmic fairness of resulting models for use in real-world adaptive systems for learning. 

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2. Math Content Readability, Student Reading Ability, and Behavior Associated with Gaming the System in Adaptive Learning Software

   https://doi.org/10.5281/zenodo.15870268  

   Khanna, Pranjli; Mathieu, Kaleb; Norberg, Kole; Almoubayyed, Husni; Fancsali, Stephen (July 2025, International Educational Data Mining Society)

   Mills, Caitlin; Alexandron, Giora; Taibi, Davide; Lo_Bosco, Giosuè; Paquette, Luc (Ed.)

   Recent research on more comprehensive models of student learning in adaptive math learning software used an indicator of student reading ability to predict students' tendencies to engage in behaviors associated with so-called "gaming the system." Using data from Carnegie Learning's MATHia adaptive learning software, we replicate the finding that students likely to experience reading difficulties are more likely to engage in behaviors associated with gaming the system. Using both observational and experimental data, we consider relationships between student reading ability, readability of specific math lessons, and behavior associated with gaming. We identify several readability characteristics of specific content that predict detected gaming behavior, as well as evidence that a prior experiment that targeted enhanced content readability decreased behavior associated with gaming, but only for students that are predicted to be less likely to experience reading difficulties. We suggest avenues for future research to better understand and model behavior of math learners, especially those who may be experiencing reading difficulties while they learn math. 

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3. Individual Chunking Ability Predicts Efficient or Shallow L2 Processing: Eye-Tracking Evidence From Multiword Units in Relative Clauses

   https://doi.org/10.3389/fpsyg.2020.607621  

   Pulido, Manuel F. (January 2021, Frontiers in Psychology)

   null (Ed.)

   Behavioral studies on language processing rely on the eye-mind assumption, which states that the time spent looking at text is an index of the time spent processing it. In most cases, relatively shorter reading times are interpreted as evidence of greater processing efficiency. However, previous evidence from L2 research indicates that non-native participants who present fast reading times are not always more efficient readers, but rather shallow parsers. Because earlier studies did not identify a reliable predictor of variability in L2 processing, such uncertainty around the interpretation of reading times introduces a potential confound that undermines the credibility and the conclusions of online measures of processing. The present study proposes that a recently developed modulator of online processing efficiency, namely, chunking ability, may account for the observed variability in L2 online reading performance. L1 English – L2 Spanish learners’ eye movements were analyzed during natural reading. Chunking ability was predictive of overall reading speed. Target relative clauses contained L2 Verb-Noun multiword units, which were manipulated with regards to their L1-L2 congruency. The results indicated that processing of the L1-L2 incongruent units was modulated by an interaction of L2 chunking ability and level of knowledge of multiword units. Critically, the data revealed an inverse U-shaped pattern, with faster reading times in both learners with the highest and the lowest chunking ability scores, suggesting fast integration in the former, and lack of integration in the latter. Additionally, the presence of significant differences between conditions was correlated with individual chunking ability. The findings point at chunking ability as a significant modulator of general L2 processing efficiency, and of cross-language differences in particular, and add clarity to the interpretation of variability in the online reading performance of non-native speakers. 

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4. Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency

   https://doi.org/10.1609/aaai.v38i20.30211  

   Haghighat, Parian; Gándara, Denisa; Kang, Lulu; Anahideh, Hadis (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Predictive analytics has been widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque and incomprehensible to the officials who use them, reducing their trust and utility. Furthermore, predictive models may introduce or exacerbate bias and inequity, as they have done in many sectors of society. Therefore, there is a need for transparent, interpretable, and fair predictive models that can be easily adopted and adapted by different stakeholders. In this paper, we propose a fair predictive model based on multivariate adaptive regression splines (MARS) that incorporates fairness measures in the learning process. MARS is a non-parametric regression model that performs feature selection, handles non-linear relationships, generates interpretable decision rules, and derives optimal splitting criteria on the variables. Specifically, we integrate fairness into the knot optimization algorithm and provide theoretical and empirical evidence of how it results in a fair knot placement. We apply our fairMARS model to real-world data and demonstrate its effectiveness in terms of accuracy and equity. Our paper contributes to the advancement of responsible and ethical predictive analytics for social good. 

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5. Mastery Learning in Undergraduate Engineering Courses: A Systematic Review

   Perez Leon, C.; Verdin, D. (August 2022, Zone 1 Conference of the American Society for Engineering Education)

   This theory paper focuses on understanding how mastery learning has been implemented in undergraduate engineering courses through a systematic review. Academic environments that promote learning, mastery, and continuous improvement rather than inherent ability can promote performance and persistence. Scholarship has argued that students could achieve mastery of the course material when the time available to master concepts and the quality of instruction was made appropriate to each learner. Increasing time to demonstrate mastery involves a course structure that allows for repeated attempts on learning assessments (i.e., homework, quizzes, projects, exams). Students are not penalized for failed attempts but are rewarded for achieving eventual mastery. The mastery learning approach recognizes that mastery is not always achieved on first attempts and learning from mistakes and persisting is fundamental to how we learn. This singular concept has potentially the greatest impact on students’ mindset in terms of their belief they can be successful in learning the course material. A significant amount of attention has been given to mastery learning courses in secondary education and mastery learning has shown an exceptionally positive effect on student achievement. However, implementing mastery learning in an undergraduate course can be a cumbersome process as it requires instructors to significantly restructure their assignments and exams, evaluation process, and grading practices. In light of these challenges, it is unclear the extent to which mastery learning has been implemented in undergraduate engineering courses or if similar positive effects can be found. Therefore, we conducted a systematic review to elucidate, how in the U.S., (1) has mastery learning been implemented in undergraduate engineering courses from 1990 to the present time and (2) the student outcomes that have been reported for these implementations. Using the systematic process outlined by Borrego et al. (2014), we surveyed seven databases and a total of 584 articles consisting of engineering and non-engineering courses were identified. We focused our review on studies that were centered on applying the mastery learning pedagogical method in undergraduate engineering courses. All peer-reviewed and practitioner articles and conference proceedings that were within our scope were included in the synthetization phase of the review. Most articles were excluded based on our inclusion and exclusion criteria. Twelve studies focused on applying mastery learning to undergraduate engineering courses. The mastery learning method was mainly applied on midterm exams, few studies used the method on homework assignments, and no study applied the method to the final exam. Students reported an increase in learning as a result of applying mastery learning. Several studies reported that students’ grades in a traditional final exam were not affected by mastery learning. Students’ self-reported evaluation of the course suggests that students prefer the mastery learning approach over traditional methods. Although a clear consensus on the effect of the mastery learning approach could not be achieved as each article applied different survey instruments to capture students’ perspectives. Responses to open-ended questions have mixed results. Two studies report more positive student comments on opened-ended questions, while one study report receiving more negative comments regarding the implementation of the mastery learning method. In the full paper we more thoroughly describe the ways in which mastery learning was implemented along with clear examples of common and divergent student outcomes across the twelve studies. 

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