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Navigating time and space: examining the impact of contextual factors and self-regulation strategies on university students’ momentary engagement
Educational Psychology
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This study explored the relationships among regulated learning, teaching presence and student engagement in blended learning. A two-level model was designed based on contextual factors (teaching presence) and individual factors (regulated learning), and experience sampling method was employed to collect intensive longitudinal data on 139 participants across three universities over 13 weeks in a blended course. Furthermore, multilevel regression analysis were conducted to examine the effects of teaching presence, self-regulated learning (SRL), co-regulated learning (CoRL) on intra- and interindividual variance in student engagement. The findings were as follows. 1) Perceived teacher support and instructional design fit had a significant positive effect on cognitive and emotional engagement and were crucial contextual factors that influenced intraindividual variance in learning engagement. 2) SRL and CoRL were copredictors of student engagement in blended learning. CoRL was more related to emotional engagement, while SRL was more related to cognitive engagement. 3) Modality had a significant effect on cognitive engagement but not on emotional engagement. 4) SRL and CoRL positively moderated the relationship between perceived teaching presence and cognitive engagement, while they negatively moderated the relationship between teacher support and emotional engagement, i.e., the relation between teacher support and emotional engagement was stronger in situations of low SRL or CoRL. Implications for teaching practice on blended learning were also discussed.
Supplementary information:
The online version contains supplementary material available at 10.1007/s10639-023-11717-5.
Self-regulated learning (SRL) plays a critical role in asynchronous online courses. In recent years, attention has been focused on identifying student subgroups with different patterns of online SRL behaviors and comparing their learning performance. However, there is limited research leveraging traces of SRL behaviors to detect student subgroups and examine the subgroup differences in cognitive load and student engagement. The current study tracked the engagement of 101 graduate students with SRL-enabling tools integrated into an asynchronous online course. According to the recorded SRL behaviors, this study identified two distinct student subgroups, using sequence analysis and cluster analysis: high SRL (H-SRL) and low SRL (L-SRL) groups. The H-SRL group showed lower extraneous cognitive load and higher learning performance, germane cognitive load, and cognitive engagement than the L-SRL group did. Additionally, this study articulated and compared temporal patterns of online SRL behaviors between the student subgroups combining lag sequential analysis and epistemic network analysis. The results revealed that both groups followed three phases of self-regulation but performed off-task behaviors. Additionally, the H-SRL group preferred activating mastery learning goals to improve ethical knowledge, whereas the L-SRL group preferred choosing performance-avoidance learning goals to pass the unit tests. The H-SRL group invested more in time management and notetaking, whereas the L-SRL group engaged more in surface learning approaches. This study offers researchers both theoretical and methodological insights. Additionally, our research findings help inform practitioners about how to design and deploy personalized SRL interventions in asynchronous online courses.
The present study investigated the qualitatively different moments of engagement and disengagement experienced by students at upper-secondary school in academic situations. Further, we examined between-student differences in the occurrence of these moments and their associations with both momentary task performance and overall academic achievement. By means of multilevel latent profile analysis (MLPA) we examined 1392 momentary experiences of 130 students, collected via experience sampling method (ESM). We identified six types of (dis)engagement moments varying within students from moment to moment: high engagement, moderate engagement, indifferent engagement, anxious engagement, anxious disengagement and bored disengagement. In addition, we identified four student profiles: highly engaged, moderately engaged, indifferently engaged and anxious. Whereas the engagement moments were related to momentary task performance, there were no differences between the student profiles in academic achievement. These results shed light on the nuanced nature of engagement and disengagement, and how they vary across individuals and situations.
Situational engagement plays a critical role in promoting students' academic performance. In a smart classroom environment, this study collected longitudinal real‐time data for 105 college students at a university in central China to investigate the relationship among situational engagement, personal characteristics and learning environment perceptions. Hierarchical linear modelling showed that environmental perception and students' personal factors have different effects on situational engagement. Specifically, (1) social support perceptions, autonomous motivation and controlled motivation have a significant impact on behavioural engagement; (2) perceptions of social and media support, autonomous motivation and controlled motivation have a significant impact on shallow cognitive engagement; and (3) perceptions of teacher and social support, self‐efficacy and autonomous motivation significantly predict deep cognitive and emotional engagement. This study suggests that the effect of the perception regarding advanced technology‐supported learning environments on students' situational engagement is limited, and instructors should pay more attention to improving students' perceptions of teacher and social support, self‐efficacy and autonomous motivation to promote students' situational deep cognitive engagement in smart classrooms.
Practitioner notes
What is already known about this topic Compared with overall engagement, situational engagement fluctuates and changes with time and context.
Situational engagement is a product of environmental and personal factors.
Few studies have focused on the nature of situational engagement and how environmental and personal factors influence situational engagement in smart classrooms.
What the paper adds This study contributes to the existing literature by investigating the critical factors that predict situational engagement, using the experience sampling method in a smart classroom at a Chinese university.
Environmental perception, self‐efficacy and students' motivation factors have different effects on situational engagement in a smart classroom.
Perceptions of teacher and social support, self‐efficacy and autonomous motivation significantly predict deep cognitive and emotional engagement, while perceptions of media support only have a significant impact on shallow cognitive engagement.
Personal factors (controlled and autonomous motivation) moderate the relationship between environmental perception factors and situational engagement.
Implications for practice and/or policy Rather than only providing external technology‐rich conditions, instructors should focus more on improving students' perceptions of teacher and social support, self‐efficacy and autonomous motivation in the smart classroom environment.
Instructors should promote students' perception of teacher support and their autonomous motivation to enhance their deep cognitive engagement.
Achievement emotions are emotions linked to academic, work, or sports achievement activities (activity emotions) and their success and failure outcomes (outcome emotions). Recent evidence suggests that achievement emotions are linked to motivational, self-regulatory, and cognitive processes that are crucial for academic success. Despite the importance of these emotions, syntheses of empirical findings investigating their relation with student achievement are scarce. We broadly review the literature on achievement emotions with a focus on activity-related emotions including enjoyment, anger, frustration, and boredom, and their links to educational outcomes with two specific aims: to aggregate all studies and determine how strongly related those emotions are to academic performance, and to examine moderators of those effects. A meta-analytical review was conducted using a systematic database of 68 studies. The 68 studies included 57 independent samples for enjoyment (N = 31,868), 25 for anger (N = 11,153), 9 for frustration (N = 1418), and 66 for boredom (N = 28,410). Results indicated a positive relation between enjoyment of learning and academic performance (ρ = .27), whereas the relations were negative for both anger (ρ = − .35) and boredom (ρ = − .25). For frustration, the relation with performance was near zero (ρ = − .02). Moderator tests revealed that relations of activity emotions with academic performance are stronger when (a) students are in secondary school compared with both primary school and college, and (b) the emotions are measured by the Achievement Emotions Questionnaires – Mathematics (AEQ-M). Theoretical and practical implications are discussed.
We investigated grit and its relations with students’ self-regulated learning (SRL) and academic achievement. An ethnically diverse sample of 213 college students completed an online self-report survey that included the Grit Short scale (Duckworth and Quinn Journal of Personality Assessment, 91(2), 166–174, 2009), seven indicators of SRL and their past and present academic achievement. Results indicated that one aspect of grit, perseverance of effort, was a consistent and adaptive predictor for all indicators of SRL including value, self-efficacy, cognitive, metacognitive, motivational, time and study environment management strategies, and procrastination. A second aspect of grit, consistency of interest, was associated only with the latter two facets of SRL. Perseverance of effort predicted achievement before, but not after, accounting for SRL; hence, students’ engagement in SRL may serve as a mediating pathway through which this aspect of grit is associated with improved academic outcomes. In contrast, consistency of interest showed no relation to achievement. Implications of the findings for additional research and instruction are discussed.
Despite its recognized importance for academic success, much of the research investigating time management has proceeded without regard to a comprehensive theoretical model for understanding its connections to students’ engagement, learning, or achievement. Our central argument is that self-regulated learning provides the rich conceptual framework necessary for understanding college students’ time management and for guiding research examining its relationship to their academic success. We advance this larger purpose through four major sections. We begin by describing work supporting the significance of time management within post-secondary contexts. Next, we review the limited empirical findings linking time management and the motivational and strategic processes viewed as central to self-regulated learning. We then evaluate conceptual ties between time management and processes critical to the forethought, performance, and post-performance phases of self-regulated learning. Finally, we discuss commonalities in the antecedents and contextual determinants of self-regulated learning and time management. Throughout these sections, we identify avenues of research that would contribute to a greater understanding of time management and its fit within the framework of self-regulated learning. Together, these efforts demonstrate that time management is a significant self-regulatory process through which students actively manage when and for how long they engage in the activities deemed necessary for reaching their academic goals.
Previous research has demonstrated that student motivation and engagement can take different forms across a variety of tasks at school or college. However, no research has yet examined the forms of student momentary engagement that emerge in response to a single task. Adolescent students (N = 196) from two low-income secondary schools in Dublin, Ireland, were given the same English grammar task to complete in a ten-minute period. We used systematic observation and post-task self-report measures to collect data on momentary cognition, emotion, motivation, and behavior. Using Latent Profile Analysis, we discovered seven main forms of momentary (dis)engagement: fully engaged, attentive but amotivated, attentive but disinterested, attentive but disaffected, distracted but motivated, disengaged, and deeply disengaged. Gender, ethnicity, academic self-efficacy, peer support and classmate cognitive engagement were notable predictors of group membership. The results should be useful to educators wanting to understand why students in their classrooms have a variety of responses to the same task.
Although university students use their digital devices for almost everything, current studies shows that students have difficulties with digital learning because they lack in self-regulated skills which in return lead to low performance. Self-regulated learning strategies (SRLS) are used assist students to learn efficiently. While many researchers have investigated SRLS towards academic outcomes such as grades, little is known about the use of SRLS towards non-academic outcomes that are also essential to assist university students’ learning progression. Hence, there is a need to understand how best to utilise SRLS to drive positive non-academic outcomes in digital learning within a blended learning environment. The systematic review methodology follows PRISMA guidelines to explore the current literature. Different sources were searched using predefined search items. A total of 239 retrievals were found which were screened for duplication. A closer screening was done on the abstracts and titles of 239 papers after duplication removal. 28 full text papers were evaluated for eligibility. Finally, 14 papers were then selected for the review. Most of the papers included in the review were peer-reviewed articles published in social science and educational journals. List of self-regulated learning strategies and non-academic outcomes used in a blended learning environment in higher education institutions were identified. Majority of the 14 reviewed papers investigated metacognitive knowledge, resource management and motivational belief strategies towards learning performance whereas cognitive engagement strategies was the least researched. Results revealed that generally, SRLS positively correlate with non-academic outcomes. At the end of the review, research gap and the future direction are presented.
This article answers a call for increased scientific precision in the conceptualization of student engagement by contributing a definition of engagement at the microlevel grain size of individual students’ momentary involvement in academic tasks. We build on the Classroom Engagement Framework, and use a dynamic systems perspective to offer a conceptualization of momentary engagement by specifying its parts (emotion, motivation, mental action, and physical action), structure (how parts co-act with each other), and process (how momentary engagement unfolds as a sequence of engagement triggers, action and disengagement). We briefly review research methods suited to studying momentary engagement and discuss research questions that emerge out of conceptualizing engagement as a momentary dynamic system, focusing on engagement complexity, emergence and dynamics. We conclude by calling for more research informed by a dynamic systems approach to hasten a paradigm shift in research on student engagement.
This study focuses on the situational heterogeneity of motivation by investigating in-the-moment profiles of expectancies, task values, and costs within learning situations during a university lecture. In a sample of 155 undergraduate students followed across one semester we examined the occurrence of six hypothesized profiles, situational profile change, and the associations of situational motivation profiles with students’ dispositional motivation. Results of multilevel latent profile analysis revealed three profiles with symmetric levels of expectancies, values, and costs (reflecting high, medium, and low motivation situations), and one profile reflecting motivating but costly situations. Furthermore, situational profiles were associated with students’ motivational dispositions at beginning and end of the semester, and partly related to changes in these dispositions during the semester.
Attention is a cognitive process crucial for human performance. It has four components: tonic alertness, phasic alertness, selective attention, and sustained attention. All the components of attention show homeostatic (time awake, sleep deprivation) and circadian (time of day) variations. The time course of the circadian rhythms in attention is important to program work and school-related activities. The components of attention reach their lowest levels during nighttime and early hours in the morning, better levels occur around noon, and even higher levels can be observed during afternoon and evening hours. However, this time course can be modulated by chronotype, sleep deprivation, age, or drugs. Homeostatic and circadian variations have also been found in other basic cognitive processes (working memory and executive functions), with a time course similar to that observed for attention. Data reviewed in this paper suggests the need to consider circadian rhythms, age, and chronotype of the person, when programming schedules for work, study, school start time, school testing, psychological testing, and neuropsychological assessment.
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Available at:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430172/
Results of regression models, like estimates, are typically presented as tables that are easy to understand. Sometimes pure estimates are not helpful and difficult to interpret. This is especially true for interaction terms in logistic regression or even more complex models, or transformed terms (quadratic or cubic terms, polynomials, splines), where the estimates are no longer interpretable in a direct way. In such cases, marginal effects are far easier to understand. In particular, the visualization of marginal effects makes it possible to intuitively get the idea of how predictors and outcome are associated, even for complex models.
ggeffects is an R-package that aims at easily calculating marginal effects for a broad range of different regression models. This is achieved by three core ideas that describe the philosophy of the function design: 1) Functions are type-safe and always return a data frame with the same, consistent structure; 2) there is a simple, unique approach to calculate marginal effects for many different models; 3) the package supports "labelled data" (Lüdecke 2018), which allows human readable annotations for graphical outputs. This means, users do not need to care about any expensive steps after modelling to visualize the results.
The purpose of this study was to evaluate a model for considering general and specific elements of student experience in a gateway course in undergraduate Financial Accounting in a large university on the East Coast, USA. Specifically, the study evaluated a bifactor analytic strategy including a general factor of student classroom experience, conceptualized as student engagement as rooted in flow theory, as well as factors representing specific dimensions of experience. The study further evaluated the association between these general and specific factors and both student classroom practices and educational outcomes. The sample of students (N = 407) in two cohorts of the undergraduate financial accounting course participated in the Experience Sampling Method (ESM) measuring students' classroom practices, perceptions, engagement, and perceived learning throughout the one-semester course. Course grade information was also collected. Results showed that a two-level bifactor model fit the data better than two traditional (i.e., non-bifactor) models and also avoided significant multicollinearity of the traditional models. In addition to student engagement (general factor), specific dimensions of classroom experience in the bifactor model at the within-student level included intrinsic motivation, academic intensity, salience, and classroom self-esteem. At the between-student level, specific aspects included work orientation, learning orientation, classroom self-esteem, and disengagement. Multilevel Structural Equation Modeling (MSEM) demonstrated that sitting in the front of the classroom (compared to the sitting in the back), taking notes, active listening, and working on problems during class had a positive effect on within-student variation in student engagement and attention. Engagement, in turn, predicted perceived learning. With respect to between-student effects, the tendency to sit in front seats had a significant effect on student engagement, which in turn had a significant effect on perceived learning and course grades. A significant indirect relationship of seating and active learning strategies on learning and course grade as mediated by student engagement was found. Support for the general aspect of student classroom experience was interpreted with flow theory and suggested the need for additional research. Findings also suggested that active learning strategies are associated with positive learning outcomes even in educational environments where possibilities for action are relatively constrained.
This diary study provided the first classroom-based empirical test of the relations between student perceptions of high school science teachers’ various autonomy supporting and thwarting practices and students’ motivation and engagement on a daily basis over the course of an instructional unit. Perceived autonomy supporting practices were hypothesized to predict autonomous motivation and engagement outcomes, while perceived autonomy thwarting practices were hypothesized to predict controlled motivation and disaffection outcomes. In line with this prediction, multilevel modeling results based on regular reports of 208 high school students in 41 science classes across 6 weeks suggested that 4 perceived daily supports (choice provision, consideration for student preferences and interests, rationales for importance, and question opportunities) and 1 daily thwart (use of uninteresting activities) predicted changes in daily autonomous motivation and engagement. In contrast, changes in students’ daily controlled motivation and disaffection were predicted primarily by 3 perceived daily thwarts (controlling messages, suppression of student perspectives, and use of uninteresting activities). Results also suggested that practices interacted such that the perception of thwarts generally bolstered desirable daily relationships between perceived supports and students’ motivation and the perception of supports generally mitigated undesirable daily relationships between thwarts and motivation. Supplemental exploratory results suggested that the effects of choice and suppression of student perspectives may be heterogeneous and depend on the outcome and/or the presence of other practices. Implications of the findings are discussed.
One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects - both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.
This report describes the early stages of a research study funded by the British Economic and Social Research Council within a major national research programme (Teaching and Learning Research Council). The project was designed to investigate ways of Enhancing Teaching-Learning Environments in undergraduate courses (ETL project). The researchers came from three different universities (Edinburgh, Durham and Coventry) and the project was directed by Noel Entwistle and Dai Hounsell. This first project report describes the design and initial use of two inventories, one designed to assess student learning characteristics and the other focusing more on their perceptions of the teaching and other aspects of the learning environment experienced by the students. Later findings from the project can be found in the book "Teaching for Understanding at University" (Entwistle, 2009, Palsgrave Macmillan).
ABSTRACT
While focus on quality in Danish higher education has been growing in recent years, limited attention has been devoted to developing and thoroughly validating instruments that allow collecting data about university students’ perceptions of the teaching-learning environment. Based on data from a large sample of Danish university students, a Danish version of the Learn questionnaire was validated using confirmatory and exploratory factor analysis. Analyses confirmed the existence of three scales reflecting students’ approaches to learning and six scales reflecting students’ perception of the teaching-learning environment. The results suggest that the Danish version of Learn is a valid instrument to be used for evaluation of the teaching-learning environment as well as for research into Danish university students’ learning strategies.
Research spanning 20 years is reviewed as it relates to the measurement of cognitive engagement using self-report scales. The author's research program is at the forefront of the review, although the review is couched within the broader context of the research on motivation and cognitive engagement that began in the early 1990s. The theoretical origins of self-report instruments are examined, along with the early measurement findings and struggles. Research in science, technology, engineering, and mathematics contexts are highlighted. The author concludes that self-report data have made significant and important contributions to the understanding of motivation and cognitive engagement. However, the evidence also suggests a need to develop and use multiple approaches to measuring engagement in academic work rather than rely only on self-report instruments. Some alternatives to self-report measures are suggested here and throughout this issue.
Engagement is one of the hottest research topics in the field of educational psychology. Research shows that multifarious benefits occur when students are engaged in their own learning, including increased motivation and achievement. However, there is little agreement on a concrete definition and effective measurement of engagement. This special issue serves to discuss and work toward addressing conceptual and instrumentation issues related to engagement, with particular interest in engagement in the domain of science learning. We start by describing the dimensional perspective of engagement (behavioral, cognitive,
emotional, agentic) and suggest a complementary approach that places engagement instrumentation on a continuum. Specifically, we recommend that instrumentation be considered on a “grain-size” continuum that ranges from a person-centered to a context-centered orientation to clarify measurement issues. We then provide a synopsis of the articles included in this special issue and conclude with suggestions for future research.
Maximum likelihood or restricted maximum likelihood (REML) estimates of the
parameters in linear mixed-effects models can be determined using the lmer
function in the lme4 package for R. As for most model-fitting functions in R,
the model is described in an lmer call by a formula, in this case including
both fixed- and random-effects terms. The formula and data together determine a
numerical representation of the model from which the profiled deviance or the
profiled REML criterion can be evaluated as a function of some of the model
parameters. The appropriate criterion is optimized, using one of the
constrained optimization functions in R, to provide the parameter estimates. We
describe the structure of the model, the steps in evaluating the profiled
deviance or REML criterion, and the structure of classes or types that
represents such a model. Sufficient detail is included to allow specialization
of these structures by users who wish to write functions to fit specialized
linear mixed models, such as models incorporating pedigrees or smoothing
splines, that are not easily expressible in the formula language used by lmer.
The University of Helsinki, along with the other European universities, is facing challenges for enhancing the quality of teaching and developing quality assurance systems with comparable criteria. To tackle these aims the university started to develop a student feedback system with a solid theoretical feedback and valuable practical implications. The present study describes the process of developing a research instrument and a questionnaire as a part of a research project The Students’ Approaches to Learning and their Experiences of the Teaching-Learning Environment(s) (LEARN) at the university. The work carried out at the University of Helsinki clearly demonstrates the value of using the LEARN questionnaire: at the same time it is a valid research instrument and a practical tool for enhancing the quality of students’ learning. Different faculties at the University of Helsinki have used the instrument for research purposes, in their quality work and in student counselling. Furthermore, the software that enables the systematic use of the instrument has been developed and it will be used across the University of Helsinki.
While the widespread acceptance of social virtual words is being increased in the last years, little are known about how students’ personal factors can affect their engagement in online learning courses. The current study proposed and empirically examined a conceptual model that aimed to fill this gap. The main purpose is to present an extensive empirical data of 305 novice or expert students (153 graduates and 152 postgraduates) who enrolled in online courses at university level which were held in Second Life. On this occasion it was tried to be investigated, measured and finally verified the effects of computer self-efficacy, metacognitive self-regulation and self-esteem that can predict the students’ engagement as an overall multidimensional construct of factors (cognitive, emotional and behavioral). The results from the three-step hierarchical regression analysis revealed that computer self-efficacy, metacognitive self-regulation, and self-esteem in online courses were not only positively correlated with student’s cognitive and emotional engagement factors, but were also negatively correlated with behavioral factors. Educational implications from these results can provide a more expedient and meritorious instructional quality format aimed at reinforcing users’ engagement in Second Life for sequencing and pacing future-driven online courses.
Background:
The motivation and emotions of students are context dependent. There are specific moments when students may find their coursework more or less motivating, resulting in stronger or milder emotional responses. Identifying factors directly controllable by teachers empowers them to effectively address challenging situations characterized by lower motivation and increased negative emotions.
Aims:
We aimed to investigate how learning activities and students' perception of teaching practices fostering autonomy relate to competence and value beliefs, and emotions in the context of course participation within higher education.
Sample:
Seventy-seven Taiwanese university students provided 762 learning reports associated with their course participation experiences.
Methods:
The experience sampling method (ESM) was used. Participants responded to ESM surveys on their phones for 14 days, reporting motivational beliefs, emotions and contextual characteristics of the course if they indicated active participation in a course upon receiving notifications from their phones.
Results:
A significant portion of the variation is attributed to situational fluctuation, suggesting that academic emotions and competence and value beliefs vary within students across measurements. An increase in students' perception of an autonomy-supportive learning climate correlates with higher competence beliefs, intrinsic value and positive emotions, coupled with reduced perceived costs and negative emotions. In contrast to lectures, engaging in independent hands-on work, participating in group collaborative projects or interactive discussions appear to inspire motivation or evoke stronger emotional responses in students.
Conclusions:
Teachers' teaching practices and classroom learning activities play a pivotal role in shaping students' situational motivation and emotions.
Student motivation varies quickly, particularly under the pressing context. However, extant literature tends to focus on the individual, rather than contextual, differences in motivational patterns. We examined Korean adolescents’ time-varying pursuits of achievement goals and learning outcomes using the experience sampling method, which collects individuals’ subjective experiences repeatedly across time and context. A hyperlink to an 11-item online survey asking about achievement goals and learning outcomes at the moment was sent to 45 adolescents twelve times during the weekend before the midterm examination. The multilevel data revealed that mastery-approach goals predicted cognitive strategies better at the between-person level where they were treated as stable individual characteristics. In comparison, ability-approach and -avoidance goals retained their predictive power at the within-person level where their time-dependent changes significantly predicted several outcomes. The results demonstrate how adolescents’ achievement goals fluctuated during a short time and how these changes predicted different learning outcomes.
Integrating the two dominant theories of self-regulated learning (SRL) and cognitive engagement could advance our understanding of what makes students more efficient, effective learners. An integration of these theories has yet to be explored, and this paper addresses this gap by proposing a novel integrative model of SRL engagement. Specifically, we identified the nature of cognitive engagement (i.e., changing consecutively, context-dependent, comprising quantitative and qualitative dimensions, occurring consciously or unconsciously), based on which we compared the conceptual differences and similarities between cognitive engagement and SRL. We reviewed three models that have investigated cognitive engagement within the frameworks of SRL, analyzed their features and weaknesses, and proposed an extension of previous models linking SRL and cognitive engagement. The proposed model is one of the first to clarify the mechanisms of how SRL phases and subprocesses relate to the functioning of cognitive engagement. In addition to adding to the theoretical discussions of the relations between cognitive engagement and SRL, the model informs the design of adaptive scaffoldings and the practice of learning analytics. Several recommendations are presented for future research in this area to test this new model empirically.
Maintaining learning engagement throughout adolescence is critical for long‐term academic success. This research sought to understand the role of metacognition and motivation in predicting adolescents' engagement in math learning over time. In two longitudinal studies with 2,325 and 207 adolescents (ages 11–15), metacognitive skills, interest, and self‐control each uniquely predicted math engagement. Additionally, metacognitive skills worked with interest and self‐control interactively to shape engagement. In Study 1, metacognitive skills and interest were found to compensate for one another. This compensatory pattern further interacted with time in Study 2, indicating that the decline in engagement was forestalled among adolescents who had either high metacognitive skills or high interest. Both studies also uncovered an interaction between metacognitive skills and self‐control, though with slightly different interaction patterns.
Out‐of‐school‐time programs for youth that are focused on STEM content are often seen as affording opportunities to increase youth engagement, interest, and knowledge in STEM domains, yet we know relatively little about how youth actually experience such programs. In this article, we explore how experiences and activities employed in the delivery of summer STEM programs are associated with youth engagement during programming, and whether youth characteristics moderate these relationships. Data were collected from 203 youth (ages 10–16) in nine summer programs using multiple methods including video, experience sampling, and surveys. Through the use of cross‐classified, multi‐level models, we found that youth reported higher engagement in program activities they perceived to be more challenging and relevant, and in activities, they perceived to have more affordances for learning or developing skills. Gender moderated these relationships such that the positive relationships observed among males were muted or nonexistent for girls. We further identify that program activities are differently associated with fostering challenge, relevance, and learning. Findings have implications for out‐of‐school STEM programming for youth.
Studying mobile learning – the use of personal electronic devices to engage in learning across multiple contexts via connections to media, educators, peers, experts, and the larger world – is a relatively new academic enterprise. In this special issue, we interrogated the promise and unexamined expectations of mobile learning, the theories and ideas developing around it, and the devices that afford it. The articles introduce mobile and wearable technologies as key components of empirical research and demonstrate ways that learning conducted with such devices (1) affects the process and products of learning via interactions with other psychological constructs; (2) affords new opportunities to directly influence learning process or outcomes; and (3) provides opportunities to collect previously unobtainable data that improve understanding and modeling of the learning process. In this introduction, we overview the emergence of mobile learning theory and its contemporary conceptualization. Then we highlight ways that mobile technologies can be used to enhance learning processes and an understanding of them. All special issue contributors conceptualize and align their work with both psychological theories of learning and instruction as well as emerging theories of mobile learning. The commentary authors appraise mobile learning research critically and analytically, and recommend ways mobile learning theory might build upon research methodology and knowledge grounded empirically in psychological and sociocultural theories of learning. Overall, we believe this special issue achieved our goal to produce a balanced consideration that highlights the advancements in learning and learning theory mobile devices might afford, and to temper any premature enthusiasm about these potential benefits.
With the affordances of mobile devices and experience-sampling method, this study took a person-in-context research orientation and examined the interactive relationship between self-efficacy, contextual features, and behavioral and cognitive engagement in authentic mobile learning contexts. Participants include 52 college students in teacher education programs. They responded to experience-sampling surveys based on the study events that they planned for the two weeks prior to exams during the semester. Regression analysis revealed that students’ course-specific self-efficacy and characteristics of planned study events were significantly associated with students’ behavioral engagement related to how well students implemented their study plans. Hierarchical regression analysis also showed that contextual features of the study environment, including study location and reasons for studying, moderated the relationship between task-specific self-efficacy and cognitive engagement. The results highlight the critical roles of self-efficacy and contextual features in influencing engagement in authentic anywhere and anytime mobile learning. The affordances and hinderances of experience-sampling method and mobile technologies in supporting engagement research were discussed.
In an investigation with 133 undergraduate students, we measured affective, cognitive, behavioral engagement, and self-regulation with a pre-survey, a post-survey, and in the moment of studying using experience-sampling methodology (ESM). We compared within these self-report techniques and also between self-reports and objective measures afforded by ESM. We found similar patterns that differed in detail. Furthermore, the ESM surveys allowed for a more fine-grained exploration of engagement related to studying behavior. Importantly, we compared fixed sampling and event-based sampling and found that the latter significantly improved sampling accuracy. Finally, we posit that a new and useful way to assess student self-regulation is the relationship between when students predict that they will study and when students report actual studying in the moment using ESM, which we call implementation rate. We were able to capture and examine all three dimensions of engagement (behavioral, cognitive and affective engagement) and self-regulation in authentic settings and in the same study, allowing us to examine the relationships among these variables exactly when learning occurs, which has several theoretical and practical implications.
The present study examines antecedents of university students' academic emotions (Pekrun, Goetz, Titz, & Perry, 2002) in the context of self-determination theory (SDT; Deci & Ryan, 1985; 2000), using real-time assessment and intra-individual analyses. We investigated whether daily autonomous and controlled-motivated educational goals predicted students' academic emotions. University students (N = 55) completed smartphone diaries over 14 consecutive days. The two-week intensive longitudinal data were organized in a hierarchical three-level structure, with situations (Level 1) nested within days (Level 2) nested within students (Level 3). Students' goal motivation was assessed in morning questionnaires, and academic emotions in three daytime questionnaires. The results of the multilevel structural equation models showed that setting self-determined autonomous educational goals predicted positive emotions, whereas controlled motivation predicted negative emotions in everyday academic situations, applying both to within-person processes and between-person differences. Both kinds of goal motivation, autonomous and controlled, were associated with determination in students’ daily lives.
Science education reform efforts in the Unites States call for a dramatic shift in the way students are expected to engage with scientific concepts, core ideas, and practices in the classroom. This new vision of science learning demands a more complex conceptual understanding of student engagement and research models that capture both the multidimensionality and contextual specificity of student engagement in science. In a unique application of person-oriented analysis of experience sampling data, we employ cluster analysis to identify six distinct momentary engagement profiles representing different combinations of the behavioral, cognitive, and affective dimensions of student engagement in high school science classrooms. Students spend a majority of their classroom time in one of several engagement profiles characterized by high engagement on one dimension, but low levels on the others. Students exhibited low engagement across all three dimensions of engagement in about 22% of our observations. Full engagement, or high levels across all three dimensions, is the least frequent profile, occurring in only 11% of the observations. Students’ momentary engagement profiles are related in meaningful ways to both the learning activity in which students are engaged and the types of choices they are afforded. Laboratory activities provided especially polarized engagement experiences, producing full engagement, universally low engagement, and pleasurable engagement in which students are affectively engaged but are not engaged cognitively or behaviorally. Student choice is generally associated with more optimal engagement profiles and the specific type of choice matters in important ways. Choices about how to frame the learning activity have the most positive effects relative to other types of choices, such as choosing whom to work with or how much time to take. Results are discussed in terms of implications for practice and the utility of the methodological approach for evaluating the complexities of student engagement in science classrooms.
It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in embodied theories of cognition and affect, which advocate a close coupling between thought and action. It uses machine-learned computational models to automatically infer mental states associated with engagement (e.g., interest, flow) from machine-readable behavioral and physiological signals (e.g., facial expressions, eye tracking, click-stream data) and from aspects of the environmental context. We present 15 case studies that illustrate the potential of the AAA approach for measuring ensgagement in digital learning environments. We discuss strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research.
In this chapter we provide an overview of the conceptual and methodological issues involved in developing and evaluating measures of metacognition and self-regulated learning. Our goal is to suggest a general framework for thinking about these assessments- a framework that will help generate questions and guide future research and development efforts. Broadly speaking, we see the main issue in assessing metacognition and self-regulated learning as one of construct validity. Of critical importance are the conceptual or theoretical definitions of these constructs and the adequacy of the empirical evidence offered to justify or support interpretations of test scores obtained from instruments designed to measure them.
In speaking to this issue of construct validity, we organize our chapter into four main sections. First, we review the various theoretical and conceptual models of metacognition and self-regulated learning and propose three general components of metacognition and selfregulation that will guide our discussion in subsequent sections. Second, we briefly describe a set of criteria proposed by Messick (1989) for investigating construct validity and suggest a set of guiding questions and general issues to consider in evaluating measures of metacognition and self-regulated learning. Third, we discuss in some detail several measures for assessing metacognition and self-regulated learning in light of the empirical evidence available to address issues of the construct validity of these measures. In the fourth and final section, we draw some conclusions about current measures of metacognition and self-regulated learning, suggest some directions for future research, and raise some issues that merit consideration in the development and evaluation of valid measures of metacognition.
This article concerns how to estimate reliability (defined as the internal consistency of responses to a scale) in designs that are commonly used in studies of within-person variability. I present relevant issues, describe common errors, make recommendations for best practice, and discuss unresolved issues and future directions. I describe how to estimate the reliability of scales administered in studies in which observations are nested within persons, such as daily diary and “beeper” studies and studies of social interaction. Multilevel modeling analyses that include a measurement level can estimate the occasionlevel (e.g., days or beeps or interactions) reliability of scales. In such models, items on a scale are nested within occasions of measurement and occasions of measurement are nested within persons.
So far, every example in this book has started with a nice dataset that’s easy to plot. That’s great for learning (because you don’t want to struggle with data handling while you’re learning visualisation), but in real life, datasets hardly ever come in exactly the right structure. To use ggplot2 in practice, you’ll need to learn some data wrangling skills. Indeed, in my experience, visualisation is often the easiest part of the data analysis process: once you have the right data, in the right format, aggregated in the right way, the right visualisation is often obvious.
In my commentary, I discuss the historical origins of the Fredricks et al. 3 dimensions of engagement, provide some critical assessment of the individual papers in this special issue, and lay out the argument for renewed theoretical analysis of the concept of engagement. Specifically, the importance of theoretical work related to the definitions of engagement, dimensionality questions, and origins of, and influences, on engagement are discussed.
Educators have long been interested in understanding the variables or factors underlying student motivation and desire to engage in and regulate their academic behaviors. In this chapter, we delineate a social-cognitive theoretical framework of self-regulatory engagement that integrates a set of highly related yet distinctive constructs such as motivation, engagement, and metacognition. Central to our self-regulation framework is a cyclical feedback loop, a process that operates in a temporal sequence (before, during, and after a learning activity) and is largely cognitive in nature. We also draw a distinction between the “will” of students to engage in learning and the “skill” with which they regulate or self-manage their level of engagement. The historical evolution and the conceptual and empirical advantages of cyclical feedback loops will be emphasized along with a description of various academic intervention programs designed to teach “cyclical” thinking and strategic behaviors to academically at-risk students. Finally, an innovative alternative assessment approach, called self-regulated learning microanalysis, is presented to illustrate how researchers and practitioners can reliably and accurately capture students’ regulatory engagement in particular contexts and settings.
Models of both self-regulated learning and student engagement have been used to help understand why some students are successful in school while others are not. The goal of this chapter is to provide greater insight into the relations between these two theoretical frameworks. The first section presents a basic model of self-regulated learning, outlining the primary phases and areas involved in that process. The next section discusses key similarities and differences between aspects of self-regulated learning and features of student engagement, drawing on both theoretical suggestions and empirical research. The final section offers ideas and avenues for additional research that would serve to better link self-regulated learning and student engagement.
Lessons learned from years of applied research in the area of student engagement, dropout prevention and school completion are offered. This article begins with a summary of theoretical constructs that guided the development of Check & Connect and continues with descriptions of multiple applications of this targeted intervention. The roles of key personnel are identified and seven core elements of the model are highlighted including the importance of “persistence plus,” relationship building and individualized intervention. Considerations for effective implementation, derived from the experiences of longitudinal implementation studies, are discussed. These insights are offered for consideration to those who are in positions to influence the educational trajectory of youth for whom school completion is likely to be difficult.
Background: Systematic literature studies have become common in software engineering, and hence it is important to understand how to conduct them efficiently and reliably.
Objective: This paper presents guidelines for conducting literature reviews using a snowballing approach, and they are illustrated and evaluated by replicating a published systematic literature review.
Method: The guidelines are based on the experience from conducting several systematic literature reviews and experimenting with different approaches.
Results: The guidelines for using snowballing as a way to search for relevant literature was successfully applied to a systematic literature review.
Conclusions: It is concluded that using snowballing, as a first search strategy, may very well be a good alternative to the use of database searches.
This chapter describes how internal high school reforms can be aimed at six different dimensions of student motivation and engagement. Students will respond to more accessible immediate rewards such as good grades and teacher praise when high schools improve with focused extra help for 8 needy students and other interventions to narrow skill gaps or recognize individual progress. Students will benefit from embedded intrinsic interest in their school program when innovations are introduced to challenge their minds and creativity. Students will find more functional relevance in their studies when high schools integrate academic and career education. Students will enjoy a more positive interpersonal climate for learning when high schools use smaller learning communities with teacher teams and advisors. Students will find opportunities to exercise their own personal nonacademic talents when schools provide more diverse electives and extracurricular activities. Students will feel more connected to shared communal norms when high schools practice fair disciplinary procedures and provide for some shared decision-making. Different combinations and sequences of high school reforms are discussed in terms of implementation strategies and the interactions of the six dimensions of student motivation and engagement. High school reform can be aimed at either the external constraints and incentives for school improvement or the internal conditions for student engagement and learning. This chapter puts reforms of the internal conditions in the context of alternative strategies for improving American high schools and examines six different aspects of student engagement in high school and how specific internal reform efforts can activate and maximize each component.
Procrastination is an educational concern for classroom instructors because of its negative psychological and academic impacts on students. However, the traditional view of procrastination as a unidimensional construct is insufficient in two regards. First, the construct needs to be viewed more broadly as time-related academic behavior, encompassing both procrastination and timely engagement. Secondly, the underlying motivation of these behaviors needs to be considered. Therefore, we developed and validated a 2 × 2 model of time-related academic behavior. The results of a confirmatory factor analysis supported a four-factor structure, and correlation with a unidimensional measure of procrastination also supported this model. Furthermore, the 2 × 2 model demonstrated significantly better fit to the data than potentially competing models. Structural equation modeling with achievement goals revealed that the 2 × 2 model unveiled relationships previously obscured in the traditional model, including that procrastination appeared to be used as a performance-enhancing strategy, while timely engagement was used to enhance mastery. The theoretical and practical implications of these new relationships are discussed.