Work-integrated learning, also known as co-operative education, allows students to alternate between on-campus classes and off-campus work terms. This provides an enhanced learning experience for students and a talent pipeline for employers. We observe that co-operative job postings are a rich source of information about the required skills,…

In this paper, we describe a theoretically-grounded data mining approach to identify types of collaborative problem solvers based on students' interactions with an online simulation-based task about electronics concepts. In our approach, we developed an ontology to identify the theoretically-grounded features of collaborative problem solving…

Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random…

In Huggins-Manley & Han (2017), it was shown that WLSMV global model fit indices used in structural equating modeling practice are sensitive to person parameter estimate RMSE and item difficulty parameter estimate RMSE that results from local dependence in 2-PL IRT models, particularly when conditioning on number of test items and sample size.…

The purpose of this pilot study was to engage pre-service elementary teachers in a semester-long project to deepen their quantitative reasoning skills when working with real-world data. Over three semesters, all pre-service elementary teachers enrolled in mathematics content courses focusing on K-8 mathematics topics had to collect, analyze,…

This manuscript focuses on the role of data analytics in mediating how teachers make sense of racial and gender inequity in patterns of student participation in their classrooms. Five middle school mathematics teachers participated in a year-long professional development program, centered on data analytics generated by the EQUIP observation tool.…

The aim of this study was to conduct a systematic literature review on the use of serious games in science education between 2016 and 2020 years. A total of 39 articles were included from Science Citation Index-Expanded (SCI-Expanded), Social Science Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI) and Emerging Sources…

Introduction: Prior to 1980, U.S. national demographic and health data collection did not identify individuals of Hispanic/Latina/o heritage as a population group. Post-1990, robust immigration from Latin America (e.g., South America, Central America, Mexico) and subsequent growth in U.S. births, dynamically reconstructed the ethnoracial lines…

I first met Jim Greer at the NATO Advanced Study Institute on Syntheses of Instructional Sciences and Computing Science for Effective Instructional Computing Systems in 1990 in Calgary, Canada. It was during this meeting that I came to realize that Jim was one of those rare individuals that could help "translate" computer science…

Students across disciplines struggle with sensemaking when they are faced with the need to understand and analyze massive amounts of information. This is particularly salient in the disciplines of both history and data science. Our approach to helping students build expertise with complex information leverages activity theory to think about the…

The NAEP EDM Competition required participants to predict efficient test-taking behavior based on log data. This paper describes our top-down approach for engineering features by means of psychometric modeling, aiming at machine learning for the predictive classification task. For feature engineering, we employed, among others, the Log-Normal…

This study investigates how youth from two cities in the United States engage in critical data practices as they learn about and take action in their lives and communities in relation to COVID-19 and its intersections with justice-related concerns. Guided by theories of critical data literacies and data justice, a historicized and future-oriented…

In this article, Susan Haarman discusses the ways in which datafication technologies such as Big Data and algorithms have the potential to either challenge or exacerbate what Miranda Fricker calls epistemic injustice. She briefly defines epistemic injustice using Fricker's subsets of testimonial and hermeneutical injustice before moving to the…

Classifying educational forum posts is a longstanding task in the research of Learning Analytics and Educational Data Mining. Though this task has been tackled by applying both traditional Machine Learning (ML) approaches (e.g., Logistics Regression and Random Forest) and up-to-date Deep Learning (DL) approaches, there lacks a systematic…

In Technology Enhanced Learning field, learning analytics cover multiple research challenges, among which tracking data analysis and data indicator design and visualization. Part of our research effort is dedicated to changing their design process, in order to capitalize them. This would allow us to meet a need in cost savings of design workflow…