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What college students say, and what they do: Aligning self-regulated learning theory with behavioral logs

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Abstract

A central concern in learning analytics specifically and educational research more generally is the alignment of robust, coherent measures to well-developed conceptual and theoretical frameworks. Capturing and representing processes of learning remains an ongoing challenge in all areas of educational inquiry and presents substantive considerations on the nature of learning, knowledge, and assessment & measurement that have been continuously refined in various areas of education and pedagogical practice. Learning analytics as a still developing method of inquiry has yet to substantively navigate the alignment of measurement, capture, and representation of learning to theoretical frameworks despite being used to identify various practical concerns such as at risk students. This study seeks to address these concerns by comparing behavioral measurements from learning management systems to established measurements of components of learning as understood through self-regulated learning frameworks. Using several prominent and robustly supported self-reported survey measures designed to identify dimensions of self-regulated learning, as well as typical behavioral features extracted from a learning management system, we conducted descriptive and exploratory analyses on the relational structures of these data. With the exception of learners' self-reported time management strategies and level of motivation, the current results indicate that behavioral measures were not well correlated with survey measurements. Possibilities and recommendations for learning analytics as measurements for self-regulated learning are discussed.

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... The present study also contributes to a central issue in LA, that is, the measurement of key learning constructs, such as SRL [9,29]. At the heart of this issue are two questions. ...
... As noted in our review, researchers are critical of students' selfreports of SRL, particularly self-report surveys [37]. However, selfreport surveys continue to be a mainstay in SRL research [29]. Our triangulation study offers a way to study the effects of feedback on SRL, by capturing self-reports of SRL through focus group discussions and analysing responses thematically using the framework of SRL. ...
... Finally, recent research (e.g., [2,29,31]) found that the analysis of trace data was aligned with SRL to some extent, notably in relation to time management. However, Quick et al. [29] also noted that behavioural data could not capture the breadth of SRL processes that were addressed in self-report surveys. ...
... Two channels of data are relevant to the work presented here: choice and order of clinical skills in time (learning paths) and text with student reflections. Learning paths can be analysed to support student reflection (Molenaar et al., 2020) or to link to self-reported self-measures (Quick et al., 2020). Learning paths are linked to process mining (see next section). ...
... Studies have linked process mining with self-regulated learning. For example, mapping out sequences of students' self-regulatory behaviours when interacting with a hypermedia program (Bannert et al., 2014), application of process mining to MOOC data and identification of six interaction sequence patterns matched to SRL strategies (Maldonado-Mahauad et al., 2018), and reports of correlations between self-reported SRL measures and behavioural traces in MOOCs (Quick et al., 2020). Process mining was also used to study temporal aspects of SRL using learners' learning management system data, comparing high-and low-performing students (Saint et al., 2020), and to detect sequences of students' modes of study to understand time management tactics and sequences of students' learning actions linked to learning tactics . ...
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... Previous research has confirmed that activity indices from LMSs' web logs provide a reliable representation of learner behaviour (Quick et al., 2020) and student engagement (Motz et al., 2019) in varied learning environments. Joksimović et al. (2015) used trace data to examine the effect that the number and duration of four interaction types had on the students' final grades. ...
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With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students’ online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables extracted from the LMS, which makes it hard to draw general conclusions about the mechanisms underlying student performance. We first provide an overview of the theoretical arguments used in learning analytics research and the typical predictors that have been used in recent studies. We then analyze 17 blended courses with 4,989 students in a single institution using Moodle LMS, in which we predict student performance from LMS predictor variables as used in the literature and from in-between assessment grades, using both multi-level and standard regressions. Our analyses show that the results of predictive modeling, notwithstanding the fact that they are collected within a single institution, strongly vary across courses. Thus, the portability of the prediction models across courses is low. In addition, we show that for the purpose of early intervention or when in-between assessment grades are taken into account, LMS data are of little (additional) value. We outline the implications of our findings and emphasize the need to include more specific theoretical argumentation and additional data sources other than just the LMS data.
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Self‐regulated learning is an ongoing process rather than a single snapshot in time. Naturally, the field of learning analytics, focusing on interactions and learning trajectories, offers exciting opportunities for analyzing and supporting self‐regulated learning. This special section highlights the current state of research at the intersection of self‐regulated learning and learning analytics, bridging communities, disciplines, and schools of thought. In this opening article, we introduce the papers and identify themes and challenges in understanding and supporting self‐ regulated learning in interactive learning environments.
Article
Individuals with strong self-regulated learning (SRL) skills, characterized by the ability to plan, manage and control their learning process, can learn faster and achieve higher grades compared to those with weaker SRL skills. SRL is critical in learning environments that provide low levels of support and guidance, as is commonly the case in Massive Open Online Courses (MOOCs). Learners can be trained to engage in SRL and further supported by facilitating prompts, activities, and tools. However, effective implementation of learner support systems in MOOCs requires an understanding of which SRL strategies are most effective and how these strategies manifest in learner behavior. Moreover, identifying learner characteristics that are predictive of weaker SRL skills can advance efforts to provide targeted support without obtrusive survey instruments. We investigated SRL in a sample of 4831 learners across six MOOCs based on individual records of overall course achievement, interactions with course content, and survey responses. Results indicated that goal setting and strategic planning predicted attainment of personal course goals, while help seeking appeared to be counterproductive. Learners with stronger SRL skills were more likely to revisit previously studied course materials, especially course assessments. Several learner characteristics, including demographics and motivation, predicted learners’ SRL skills. We discuss implications and next steps towards online learning environments that provide targeted support and guidance.
Article
Learning Analytics is an emerging research field and design discipline that occupies the “middle space” between the learning sciences/educational research and the use of computational techniques to capture and analyze data (Suthers & Verbert, 2013). We propose that the literature examining the triadic relationships between epistemology (the nature of knowledge), pedagogy (the nature of learning and teaching), and assessment provide critical considerations for bounding this middle space. We provide examples to illustrate the ways in which the understandings of particular analytics are informed by this triad. As a detailed worked example of how one might design analytics to scaffold a specific form of higher order learning, we focus on the construct of epistemic beliefs: beliefs about the nature of knowledge. We argue that analytics grounded in a pragmatic, socio-cultural perspective are well placed to explore this construct using discourse-centric technologies. The examples provided throughout this paper, through emphasizing the consideration of intentional design issues in the middle space, underscore the “interpretative flexibility” (Hamilton & Feenberg, 2005) of new technologies, including analytics.
Article
Massive open online courses (MOOCs) require individual learners to be able to self-regulate their learning, determining when and how they engage. However, MOOCs attract a diverse range of learners, each with different motivations and prior experience. This study investigates the self-regulated learning (SRL) learners apply in a MOOC, in particular focusing on how learners' motivations for taking a MOOC influence their behaviour and employment of SRL strategies. Following a quantitative investigation of the learning behaviours of 788 MOOC participants, follow-up interviews were conducted with 32 learners. The study compares the narrative descriptions of behaviour between learners with self-reported high and low SRL scores. Substantial differences were detected between the self-described learning behaviours of these two groups in five of the sub-processes examined. Learners' motivations and goals were found to shape how they conceptualised the purpose of the MOOC, which in turn affected their perception of the learning process.
Article
Massive Open Online Courses (MOOCs) require individual learners to self-regulate their own learning, determining when, how and with what content and activities they engage. However, MOOCs attract a diverse range of learners, from a variety of learning and professional contexts. This study examines how a learner's current role and context influences their ability to self-regulate their learning in a MOOC: Introduction to Data Science offered by Coursera. The study compared the self-reported self-regulated learning behaviour between learners from different contexts and with different roles. Significant differences were identified between learners who were working as data professionals or studying towards a higher education degree and other learners in the MOOC. The study provides an insight into how an individual's context and role may impact their learning behaviour in MOOCs.
Article
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed.
Article
Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.
Article
Data integration is a crucial element in mixed methods analysis and conceptualization. It has three principal purposes: illustration, convergent validation (triangulation), and the development of analytic density or “richness.” This article discusses such applications in relation to new technologies for social research, looking at three innovative forms of data integration that rely on computational support: (a) the integration of geo-referencing technologies with qualitative software, (b) the integration of multistream visual data in mixed methods research, and (c) the integration of data from qualitative and quantitative methods.
Article
Recently, learning analytics (LA) has drawn the attention of academics, researchers, and administrators. This interest is motivated by the need to better understand teaching, learning, “intelligent content,” and personalization and adaptation. While still in the early stages of research and implementation, several organizations (Society for Learning Analytics Research and the International Educational Data Mining Society) have formed to foster a research community around the role of data analytics in education. This article considers the research fields that have contributed technologies and methodologies to the development of learning analytics, analytics models, the importance of increasing analytics capabilities in organizations, and models for deploying analytics in educational settings. The challenges facing LA as a field are also reviewed, particularly regarding the need to increase the scope of data capture so that the complexity of the learning process can be more accurately reflected in analysis. Privacy and data ownership will become increasingly important for all participants in analytics projects. The current legal system is immature in relation to privacy and ethics concerns in analytics. The article concludes by arguing that LA has sufficiently developed, through conferences, journals, summer institutes, and research labs, to be considered an emerging research field.
Article
As adults we believe that our knowledge of our own psychological states is substantially different from our knowledge of the psychological states of others: First-person knowledge comes directly from experience, but third-person knowledge involves inference. Developmental evidence suggests otherwise. Many 3-year-old children are consistently wrong in reporting some of their own immediately past psychological states and show similar difficulties reporting the psychological states of others. At about age 4 there is an important developmental shift to a representational model of the mind. This affects children's understanding of their own minds as well as the minds of others. Our sense that our perception of our own minds is direct may be analogous to many cases where expertise provides an illusion of direct perception. These empirical findings have important implications for debates about the foundations of cognitive science.
Article
JAGS analyzes Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS has three aims: to have a cross-platform engine for the BUGS language; to be extensible, allowing users to write their own functions, distributions and samplers; and to be a platform for experimentation with ideas in Bayesian modeling.
Article
Anderson, Reder, and Simon (1996) contested four propositions that they incorrectly called “claims of situated learning.” This response argues that the important differences between situative and cognitive perspectives are not addressed by discussion of these imputed claims. Instead, there are significant differences in the framing assumptions of the two perspectives. I clarify these differences by inferring questions to which Anderson et al.'s discussion provided answers, by identifying presuppositions of those questions made by Anderson et al., and by stating the different presuppositions and questions that I believe are consistent with the situative perspective. The evidence given by Anderson et al. is compatible with the framing assumptions of situativity; therefore, deciding between the perspectives will involve broader considerations than those presented in their article. These considerations include expectations about which framework offers the better prospect for developing a unified scientific account of activity considered from both social and individual points of view, and which framework supports research that will inform discussions of educational practice more productively. The cognitive perspective takes the theory of individual cognition as its basis and builds toward a broader theory by incrementally developing analyses of additional components that are considered as contexts. The situative perspective takes the theory of social and ecological interaction as its basis and builds toward a more comprehensive theory by developing increasingly detailed analyses of information structures in the contents of people's interactions. While I believe that the situative framework is more promising, the best strategy for the field is for both perspectives to be developed energetically.
Article
The psychometric properties and multigroup measurement invariance of scores on the Self-Efficacy for Self-Regulated Learning Scale taken from Bandura's Children's Self-Efficacy Scale were assessed in a sample of 3,760 students from Grades 4 to 11. Latent means differences were also examined by gender and school level. Results reveal a unidimensional construct with equivalent factor pattern coefficients for boys and girls and for students in elementary, middle, and high school. Elementary school students report higher self-efficacy for self-regulated learning than do students in middle and high school. The latent factor is related to self-efficacy, self-concept, task goal orientation, apprehension, and achievement.
Article
The psychometric properties of an instrument designed to assess study behaviors of college and university students were examined. A convenience sample of 1052 undergraduates at a group of midwestern colleges and universities and at a four-year college in a United States Caribbean territory responded to the Study Behavior Inventory (SBI), Form D. A series of factor analyses using the principal components model with iteration and varimax rotations yielded three factors composed of items which appear to deal with feelings of competence, preparation for daily routine academic tasks, and preparation for special academic tasks (e.g., term papers and examinations). Internal consistency reliability estimates for the entire instrument and the items in each of the three factors ranged from .70 to .88. The findings indicated that the SBI is a valid and reliable instrument for assessing study behaviors. It is suggested that providers of developmental education and other study skills program should consider including a strong counseling component in their offerings and that it may be useful to view study behaviors as consisting of two sets of activities directed toward short term, routine goals and toward long range, specific goals, respectively. References and tables are appended. (Author/PN)
Article
purpose of this paper is to identify some of the current issues in learning strategies assessment and to present a description of the design and development of a specific instrument created to address some problems encountered in diagnosing student deficits several research and practical issues related to self-report inventories that assess learning strategies will be briefly examined validity of this type of instrument in an applied setting (such as a study improvement course) will also be discussed initial design and development of the Learning and Study Strategy Inventory (LASSI) will be presented (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
The study was designed to measure the relationship between probability of endorsement of personality items and the scaled social desirability of the items. Scale values were determined by applying the method of successive intervals to 140 personality trait items which had been administered to 152 subjects with pertinent instructions. The items were then administered to a different group of 140 students as a personality inventory. The proportion of "yes" answers was taken as a measure of the probability of endorsement and correlated against the social desirability scale value for the items. The high degree of relationship ( r = .871) is discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)