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Research in educational psychology involves empirical investigation into the learning process with an aim to refine psychological theories of learning and their application to real-world settings where they can be used to benefit learners. Emergent methodological processes involved in learning analytics include the study of event-based data produced by individuals in learning environments where they use technology. Paradigms for substantive-methodological synergy can be used to align the strengths of educational psychology and learning analytics research. The Journal of Educational Psychology invites such collaborations. This issue illustrates the advancements to educational theory and practice that can be attained when learning analytics practices are aligned to reflect the assumptions within psychological theories of learning and learning analytics methods including feature engineering and multimodal modeling are leveraged. Exemplars demonstrate learning analytics’ potential contribution to the refinement and application of theories of learning and motivation. Educational Impact and Implications Statement Theories about learning describe complex processes and how the ways individuals undertake them affect the understanding they obtain and performances they achieve. Many of these learning processes are difficult to observe in the naturalistic settings where people learn. When data individuals produce during learning with technologies are collected and modeled in alignment with learning theories and using learning analytics methods, they can make learning processes observable. Incorporating learning analytics into the study of learning and the development of instruction can help refine learning theories and the design of technologies that individuals use to learn. 

Undergraduates enrolled in large, active learning courses must self-regulate their learning (self-regulated learning [SRL]) by appraising tasks, making plans, setting goals, and enacting and monitoring strategies. SRL researchers have relied on self-report and learner-mediated methods during academic tasks studied in laboratories and now collect digital event data when learners engage with technology-based tools in classrooms. Inferring SRL processes from digital events and testing their validity is challenging. We aligned digital and verbal SRL event data to validate digital events as traces of SRL and used them to predict achievement in lab and course settings. In Study 1, we sampled a learning task from a biology course into a laboratory setting. Enrolled students (N = 48) completed the lesson using digital resources (e.g., online textbook, course site) while thinking aloud weeks before it was taught in class. Analyses confirmed that 10 digital events reliably co-occurred ≥70% of the time with verbalized task definition and strategy use macroprocesses. Some digital events co-occurred with multiple verbalized SRL macroprocesses. Variance in occurrence of validated digital events was limited in lab sessions, and they explained statistically nonsignificant variance in learners’ performance on lesson quizzes. In Study 2, lesson-specific digital event data from learners (N = 307) enrolled in the course (but not in Study 1) predicted performance on lesson-specific exam items, final exams, and course grades. Validated digital events also predicted final exam and course grades in the next semester (N = 432). Digital events can be validated to reflect SRL processes and scaled to explain achievement in naturalistic undergraduate education settings. Educational Impact and Implications Statement Instructors often have difficulty identifying and helping struggling students in courses with hundreds of students. Digital trace data can be used to efficiently and effectively identify struggling students in these large courses, but such data are often difficult to interpret with confidence. In our study, we found that using verbal trace data to augment and validate our inferences about the meaning of digital trace data resulted in a powerful set of predictors of students’ achievement. These validated digital trace data can be used to not only identify students in need of support in large classes, but also to understand how to target interventions to the aspects of learning that are causing students the most difficulty. 

The affordances of computer‐based learning environments make them powerful tools for conveying information in higher education. However, to most effectively use these environments, students must be adept at self‐regulating their learning. This self‐regulation is effortful, including a myriad of processes, including defining tasks, making plans, using and monitoring the efficacy of high‐quality learning strategies, and reflecting on the learning process and outcomes. Therefore, higher education instructors and course designers should design computer‐based learning environments to ease learning and free up mental resources for self‐regulation. This chapter describes how design principles from the cognitive theory of multimedia learning can facilitate learning in computer‐based learning environments and promote self‐regulated learning. Examples of the multimedia, personalization, and generative activity principles are presented to show how the cognitive theory of multimedia learning can guide design and promote students’ selection, organization, and integration of content, resulting in better understanding and more mental resources available for self‐regulated learning and the deeper learning it can afford. 

Abstract Undergraduate STEM lecture courses enroll hundreds who must master declarative, conceptual, and applied learning objectives. To support them, instructors have turned to active learning designs that require students to engage inself-regulated learning(SRL). Undergraduates struggle with SRL, and universities provide courses, workshops, and digital training to scaffold SRL skill development and enactment. We examined two theory-aligned designs of digital skill trainings that scaffold SRL and how students’ demonstration of metacognitive knowledge of learning skills predicted exam performance in biology courses where training took place. In Study 1, students’ (n = 49) responses to training activities were scored for quality and summed by training topic and level of understanding. Behavioral and environmental regulation knowledge predicted midterm and final exam grades; knowledge of SRL processes did not. Declarative and conceptual levels of skill-mastery predicted exam performance; application-level knowledge did not. When modeled by topic at each level of understanding, declarative knowledge of behavioral and environmental regulation and conceptual knowledge of cognitive strategies predicted final exam performance. In Study 2 (n = 62), knowledge demonstrated during a redesigned video-based multimedia version of behavioral and environmental regulation again predicted biology exam performance. Across studies, performance on training activities designed in alignment with skill-training models predicted course performances and predictions were sustained in a redesign prioritizing learning efficiency. Training learners’ SRL skills –and specifically cognitive strategies and environmental regulation– benefited their later biology course performances across studies, which demonstrate the value of providing brief, digital activities to develop learning skills. Ongoing refinement to materials designed to develop metacognitive processing and learners’ ability to apply skills in new contexts can increase benefits. 

Undergraduate science, technology, engineering, and mathematics (STEM) students’ motivations have a strong influence on whether and how they will persist through challenging coursework and into STEM careers. Proper conceptualization and measurement of motivation constructs, such as students’ expectancies and per- ceptions of value and cost (i.e., expectancy value theory [EVT]) and their goals (i.e., achievement goal theory [AGT]), are necessary to understand and enhance STEM persistence and success. Research findings suggest the importance of exploring multiple measurement models for motivation constructs, including traditional con- firmatory factor analysis, exploratory structural equation models (ESEM), and bifactor models, but more research is needed to determine whether the same model fits best across time and context. As such, we mea- sured undergraduate biology students’ EVT and AGT motivations and investigated which measurement model best fit the data, and whether measurement invariance held, across three semesters. Having determined the best- fitting measurement model and type of invariance, we used scores from the best performing model to predict biology achievement. Measurement results indicated a bifactor-ESEM model had the best data-model fit for EVT and an ESEM model had the best data-model fit for AGT, with evidence of measurement invariance across semesters. Motivation factors, in particular attainment value and subjective task value, predicted small yet statistically significant amounts of variance in biology course outcomes each semester. Our findings provide support for using modern measurement models to capture students’ STEM motivations and potentially refine conceptualizations of them. Such future research will enhance educators’ ability to benevolently monitor and support students’ motivation, and enhance STEM performance and career success. 

Well-designed instructional videos are powerful tools for helping students learn and prompting students to use generative strategies while learning from videos further bolsters their effectiveness. However, little is known about how individual differences in motivational factors, such as achievement goals, relate to how students learn within multimedia environments that include instructional videos and generative strategies. Therefore, in this study, we explored how achievement goals predicted undergraduate students’ behaviors when learning with instructional videos that required students to answer practice questions between videos, as well as how those activities predicted subsequent unit exam performance one week later. Additionally, we tested the best measurement models for modeling achievement goals between traditional confirmatory factor analysis and bifactor confirmatory factor analysis. The bifactor model fit our data best and was used for all subsequent analyses. Results indicated that stronger mastery goal endorsement predicted performance on the practice questions in the multimedia learning environment, which in turn positively predicted unit exam performance. In addition, students’ time spent watching videos positively predicted practice question performance. Taken together, this research emphasizes the availing role of adaptive motivations, like mastery goals, in learning from instructional videos that prompt the use of generative learning strategies. 

Abstract Using traces of behaviors to predict outcomes is useful in varied contexts ranging from buyer behaviors to behaviors collected from smart-home devices. Increasingly, higher education systems have been using Learning Management System (LMS) digital data to capture and understand students’ learning and well-being. Researchers in the social sciences are increasingly interested in the potential of using digital log data to predict outcomes and design interventions. Using LMS data for predicting the likelihood of students’ success in for-credit college courses provides a useful example of how social scientists can use these techniques on a variety of data types. Here, we provide a primer on how LMS data can be feature-mapped and analyzed to accomplish these goals. We begin with a literature review summarizing current approaches to analyzing LMS data, then discuss ethical issues of privacy when using demographic data and equitable model building. In the second part of the paper, we provide an overview of popular machine learning algorithms and review analytic considerations such as feature generation, assessment of model performance, and sampling techniques. Finally, we conclude with an empirical example demonstrating the ability of LMS data to predict student success, summarizing important features and assessing model performance across different model specifications. 

Teachers, schools, districts, states, and technology developers endeavor to personalize learning experiences for students, but definitions of personalized learning (PL) vary and designs often span multiple components. Variability in definition and implementation complicate the study of PL and the ways that designs can leverage student characteristics to reliably achieve targeted learning outcomes. We document the diversity of definitions of PL that guide implementation in educational settings and review relevant educational theories that could inform design and implementation. We then report on a systematic review of empirical studies of personalized learning using PRISMA guidelines. We identified 376 unique studies that investigated one or more PL design features and appraised this corpus to determine (1) who studies personalized learning; (2) with whom, and in what contexts; and (3) with focus on what learner characteristics, instructional design approaches, and learning outcomes. Results suggest that PL research is led by researchers in education, computer science, engineering, and other disciplines, and that the focus of their PL designs differs by the learner characteristics and targeted outcomes they prioritize. We further observed that research tends to proceed without a priori theoretical conceptualization, but also that designs often implicitly align to assumptions posed by extant theories of learning. We propose that a theoretically guided approach to the design and study of PL can organize efforts to evaluate the practice, and forming an explicit theory of change can improve the likelihood that efforts to personalize learning achieve their aims. We propose a theory-guided method for the design of PL and recommend research methods that can parse the effects obtained by individual design features within the “many-to-many-to-many” designs that characterize PL in practice. 

The COVID-19 disruption presented considerable challenges for university students, requiring the sudden need for increased engagement in remote learning environments and the ability to cope with academic and familial demands. To examine how students self-regulated their learning during the disruption, we surveyed undergraduates (n = 226) enrolled in four sections of a large biology course once during the first week of the semester, immediately after the disruption, and through the end of the semester. The results indicated significant decreases in student motivation, increases in students' perceived costs, and quadratic changes in self-reported coping strategies and mental depletion during disrupted learning. In a final model, students' self-efficacy and perceptions of cost, as well as feelings of anger and personal responsibility for family combined to form a parsimonious set of predictors that explained variance in course performance.