A growing body of work has shown that two specific study strategies help explain differences in learning and achievement in gateway courses: spacing (breaking up study sessions across multiple days) and self-testing (actively recalling information from memory). However, it is still unclear whether the benefits of these strategies are applicable in more advanced biology courses, and whether promoting effective study practices in these courses (spacing and self-testing) is related to increased use of these practices and greater learning outcomes. We studied two senior-level microbiology courses that were taught by the same instructor. Using a quasi-experimental design, one course additionally received a light-touch study skills intervention, where the instructor introduced the concepts of spacing and self-testing while also providing reminders to students about utilizing these strategies. We found that, while the intervention was not related to increased use of spacing and self-testing, both strategies were positively related to learning, as measured by the final course grade. Results from multiple regression analyses revealed that engaging in spacing throughout the course was the most consistent predictor of final course grade, even after accounting for other study strategies, demographic characteristics, and prior academic achievement. Our results add to the literature emphasizing the importance of spacing in increasing students’ achievement in STEM courses. © Springer Nature Switzerland AG 2021.

This study integrates theories of achievement motivation and emotion to investigate daily academic behavior in an undergraduate online course. Using cluster analysis and hierarchical logistic regression, we analyze profiles of task values and anticipated emotions to understand expectations and completion of academic tasks over the duration of a week. Students’ task specific interest, opportunity cost, and anticipated satisfaction and regret varied across tasks and were predictive of both their expectations of task completion and actual task completion reported the following day. The results shed light on the important role of achievement motivation as situated and dynamic, highlighting the interplay between task priorities, task values, and anticipated emotions in academic task engagement. © 2021, The Author(s).

Collaborative problem-solving (CPS) has become an essential component of today's knowledge-based, innovation-centred economy and society. As such, communication and CPS are now considered critical 21st century skills and incorporated into educational practice, policy, and research. Despite general agreement that these are important skills, there is less agreement on how to capture sociocognitive processes automatically during team interactions to gain a better understanding of their relationship with CPS outcomes. The availability of naturally occurring educational discourse data within online CPS platforms presents a golden opportunity to advance understanding about online learner sociocognitive roles and ecologies. In this paper, we explore the relationship between emergent sociocognitive roles, collaborative problem-solving skills, and outcomes. Group Communication Analysis (GCA) - a computational linguistic framework for analyzing the sequential interactions of online team communication - was applied to a large CPS dataset in the domain of science (participant N = 967; team N = 480). The ETS Collaborative Science Assessment Prototype (ECSAP) was used to measure learners' CPS skills, and CPS outcomes. Cluster analyses and linear mixed-effects modelling were used to detect learner roles, and assess the relationship between those roles on CPS skills and outcomes. Implications for future research and practice are discussed regarding sociocognitive roles and collaborative problem-solving skills.

The ability to regulate one's own learning is essential for success in online courses. Recent efforts have used clickstream data to create timely, fine-grained, and comprehensive measures of self-regulated learning (SRL) in online courses in an attempt to shed light on the process of SRL and to improve the identification of students who lack SRL skills and are at risk of low achievement. However, key questions remain: to what extent do these clickstream measures correspond to traditional self-reported measures about specific SRL constructs? Do these clickstream measures provide more information than existing self-reported measures in predicting course performance? This study used the clickstream data collected from a learning management system to measure two aspects of SRL: time management and effort regulation. We found that the clickstream measures were significantly associated with students' self-reported time management and effort regulation after the course. In addition, these clickstream measures significantly improved predictions of students' performance in the current and subsequent courses over predictions based on self-reported measures alone. These results provide evidence for the validity of the clickstream measures and guide the use of clickstream data to understand the process of SRL and identify students who might not be well served by taking classes online.

Online summer courses offer opportunities to catch-up or stay on-track with course credits for students who cannot otherwise attend face-to-face summer courses. While online courses may have certain advantages, participation patterns and student success in summer terms are not yet well understood. This quantitative study analyzed four years of institutional data cumulating in 72,441 course enrollments of 23,610 students in 433 courses during summer terms at a large public research university. Multi-level logistic regression models indicated that characteristics including gender, in-state residency, admission test scores, previous online course enrollment, and course size, among others, can influence student enrollment by course modality. Multi-way fixed effects linear regression models indicated that student grades were slightly lower in online courses compared to face-to-face courses. However, at-risk college student populations (low-income students, first-generation students, low-performing students) were not found to suffer additional course performance penalties of online course participation.

This data article includes information on institutional data at a large public research university in Southern California. In particular, data on undergraduate student enrollments in online and face-to-face courses during summer terms from 2014 to 2017 cumulating in 72,441 course enrollments from 23,610 undergraduate students in 433 courses is provided. This data includes additional information on the statistical models examining factors influencing student enrollment by course modality and the associations of course modality with course grades. This includes descriptive data and data derived from multi-level logistic regression analyses and multi-way fixed effects linear regression analyses. This data article is associated with the article Effects of course modality in summer session: Enrollment patterns and student performance in face-to-face and online classes [1]. (c) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

The presence of "big data"in higher education has led to the increasing popularity of predictive analytics for guiding various stakeholders on appropriate actions to support student success. In developing such applications, model selection is a central issue. As such, this study presents a comprehensive examination of five commonly used machine learning models in student success prediction. Using administrative and learning management system (LMS) data for nearly 2,000 college students at a public university, we employ the models to predict short-term and long-term academic success. Beyond the tradeoff between model interpretability and accuracy, we also focus on the fairness of these models with regard to different student populations. Our findings suggest that more interpretable models such as logistic regression do not necessarily compromise predictive accuracy. Also, they lead to no more, if not less, prediction bias against disadvantaged student groups than complicated models. Moreover, prediction biases against certain groups persist even in the fairest model. These results thus recommend using simpler algorithms in conjunction with human evaluation in instructional and institutional applications of student success prediction when valid student features are in place. © 2020 Owner/Author.

The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

Self-efficacy has a strong influence on the learning and motivation of science students at the postsecondary level, especially in upper division science classes, which are key to student success in science majors. This empirical mixed methods research study (N = 205) examines the associations between students’ participation in an online preparation course and student self-efficacy in organic chemistry. Qualitative content analysis indicated that students benefited from the online preparatory course in the subsequent organic chemistry course series. The analysis of students’ clickstream data indicated that students with self-efficacy ratings in the top 10th percentile exhibited more frequent and consistent engagement with relevant course materials compared to students in the bottom 10th percentile. Notably, linear regression models indicated that participation in the online preparatory course was associated with higher long-term self-efficacy for first-generation college students. These results suggest that online preparatory courses may benefit some students’ self-efficacy in demanding science courses. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Student clickstream data-time-stamped records of click events in online courses-can provide fine-grained information about student learning. Such data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resources, potentially enabling objective and rich insight into the learning experience beyond self-reports and intermittent assessments. Yet, analyses of these data often require advanced analytic techniques, as they only provide a partial and noisy record of students' actions. Consequently, these data are not always accessible or useful for course instructors and administrators. In this paper, we provide an overview of the use of clickstream data to define and identify behavioral patterns that are related to student learning outcomes. Through discussions of four studies, we provide examples of the complexities and particular considerations of using these data to examine student self-regulated learning.