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Abstract

Massive Open Online Courses, universally labelled as MOOCs, become more and more relevant in the era of digitalization of higher education. The availability of free education resources without access restrictions for a plenty of potential users has changed the learning market in a way unthinkable only few decades ago. This form of web-based education allows to track all the actions of the students, thus providing an information base to understand how students' behaviour can influence their performance. The paper proposes a structural equation model in the framework of the component-based approach to measure which are the main factors affecting students' performance (Partial Least Squares Path Modelling). The novelty of the approach is the simultaneous analysis of more than one factor that exerts an impact on the performance. The analysis is carried out on the log data of a course available on the edX MOOCs platform named FedericaX.

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... Advanced ICT-enhanced education platform, such as Learning Management Systems (LMS), video collaboration tools (Microsoft Teams, WebEx, and Zoom), offers diverse learning experiences that are used to organize course content, provide learning opportunities to diverse categories of learners, and provide flexible course delivery mechanisms for long distance and blended learning (Ain, Kaur, & Waheed, 2016;Bahri, Idris, Muis, Arifuddin, & Fikri, 2021). These technologies are the reasons for introducing Massive Open Online Courses (MOOCs), Mobile learning for students, Virtual labs that allow simulations of a physical experiment, serious games that engage and retain learners' attention, personalized blended learning using learning analytics, mobile devices for engaging students (Callaghan, Savin-Baden, McShane, & Eguiluz, 2017;Carannante, Davino, & Vistocco, 2020;Khan, Abdou, Kettunen, & Gregory, 2019). Thus, ICT continues to provide new and emerging opportunities that simplify ways of representing and delivering teaching and learning experiences in HEIs around the globe. ...
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... A number of studies have identified the significance of student satisfaction to use of the MOOCs system as a sustainability education approach. As a measure, the findings of this study confirm previous findings [27,42,62,75,76]. According to [77], the majority of e-tutoring users felt that online sources for learning English offer greater convenience and are more effective than no internet resources. ...
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Over the last 5 years, massive open online courses (MOOCs) have increasingly provided learning opportunities across the world in a variety of domains. As with many emerging educational technologies, why and how people come to MOOCs needs to be better understood and importantly what factors contribute to learners' MOOC performance. It is known that online learning environments require greater levels of self-regulation, and that high levels of motivation are crucial to activate these skills. However, motivation is a complex construct and research on how it functions in MOOCs is still in its early stages. Research presented in this article investigated how motivation and participation influence students' performance in a MOOC, more specifically those students who persist to the end of the MOOC. Findings indicated that the strongest predictor of performance was participation, followed by motivation. Motivation influenced and was influenced by students' participation during the course. Moreover, situational interest played a crucial role in mediating the impact of general intrinsic motivation and participation on performance. The results are discussed in relation to how educators and designers of MOOCs can use knowledge emerging from motivational assessments and participation measures gleaned from learning analytics to tailor the design and delivery of courses.
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This study compared students' academic procrastination tendency with the (1) frequency and nature of rationalizations used to justify procrastination, (2) self-regulation, and (3) performance in a web-based study strategies course with frequent performance deadlines. 106 college students completed the 16-item Tuckman Procrastination Scale, a measure of tendency to procrastinate, the Frequency of Use Self-survey of Rationalizations for Procrastination, and a 9-item self-regulation scale. Students' subsequent course performance was measured by total points earned. A linear regression with Academic Procrastination as the criterion variable and Rationalization score and Course Points as the predictor variables suggested academic procrastinators support procrastinating by rationalizing, not self-regulating, and thus put themselves at a disadvantage, with respect to evaluation in highly structured courses with frequent enforced deadlines.
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Partial Least Squares (PLS) based Structural Equation Modeling (SEM) has become increasingly popular in Management Information Systems (MIS) research to model complex relationships and to make valid inferences from the restricted sample to the larger population. Given the larger goal of creating generalizable theories in MIS research, we argue that the lack of model selection criteria in PLS that penalize model complexity might be causing researchers to select unnecessarily complex but highly fitting models that may not generalize to other samples. We introduce several Information Theoretic (IT) model selection criteria in the PLS context that penalize model complexity but reward high fit, and therefore guide researchers to select a parsimonious and generalizable model. In this Monte Carlo study, we compare their performance to the currently existing PLS indices, in selecting the best model among a set of competing models under various conditions of sample size, effect size and data distribution. Based on our simulation results, we strictly advise against the use of R2 and GoF based measures in PLS for model selection. Instead, we demonstrate that the IT criteria have much superior model selection rates than the currently existing PLS indices. Therefore, we recommend a core set of IT criteria that researchers should regularly employ when selecting models among a competing set of models using PLS based SEM.
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This study sought to identify significant behavioral indicators of learning using learning management system (LMS) data regarding online course achievement. Because self-regulated learning is critical to success in online learning, measures reflecting self-regulated learning were included to examine the relationship between LMS data measures and course achievement. Data were collected from 530 college students who took an online course. The results demonstrated that students' regular study, late submissions of assignments, number of sessions (the frequency of course logins), and proof of reading the course information packets significantly predicted their course achievement. These findings verify the importance of self-regulated learning and reveal the advantages of using measures related to meaningful learning behaviors rather than simple frequency measures. Furthermore, the measures collected in the middle of the course significantly predicted course achievement, and the findings support the potential for early prediction using learning performance data. Several implications of these findings are discussed.
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Patterns in student accesses of online materials and their effects upon student performance in a blended course are examined. Our blended course is an introductory business and economic statistics course where lectures are only available online while the traditional class period is used for complementary learning activities. Timing, volumes, intensity, and consistency of the student accesses of the online lectures are considered. Using bivariate and multivariate analyses, measures of timing and consistency are shown to be related to student performance but volumes and intensity of accesses are not.
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In massive open online courses (MOOCs), low barriers to registration attract large numbers of students with diverse interests and backgrounds, and student use of course content is asynchronous and unconstrained. The authors argue that MOOC data are not only plentiful and different in kind but require reconceptualization—new educational variables or different interpretations of existing variables. The authors illustrate this by demonstrating the inadequacy or insufficiency of conventional interpretations of four variables for quantitative analysis and reporting: enrollment, participation, curriculum, and achievement. Drawing from 230 million clicks from 154,763 registrants for a prototypical MOOC offering in 2012, the authors present new approaches to describing and understanding user behavior in this emerging educational context.
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The concept of school engagement has attracted increasing attention as representing a possible antidote to declining academic motivation and achievement. Engagement is presumed to be malleable, responsive to contextual features, and amenable to environmental change. Researchers describe behavioral, emotional, and cognitive engagement and recommend studying engagement as a multifaceted construct. This article reviews definitions, measures, precursors, and outcomes of engagement; discusses limitations in the existing research; and suggests improvements. The authors conclude that, although much has been learned, the potential contribution of the concept of school engagement to research on student experience has yet to be realized. They call for richer characterizations of how students behave, feel, and think—research that could aid in the development of finely tuned interventions
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Racial and ethnic achievement gaps narrowed substantially in the 1970s and 1980s. As some of the gaps widened in the 1990s, there were some setbacks in the progress the nation made toward racial and ethnic equity. This article offers a look below the surface at Black-White and Hispanic-White achievement gap trends over the past 30 years. The literature review and data analysis identify the key factors that seem to have contributed to bifurcated patterns in achievement gaps. The conventional measures of socioeconomic and family conditions, youth culture and student behavior, and schooling conditions and practices might account for some of the achievement gap trends for a limited time period or for a particular racial and ethnic group. However, they do not fully capture the variations. This preliminary analysis of covariations in racial and ethnic gap patterns across several large data sets has implications for future research on the achievement of minority groups.
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The present study examines whether the relationship between work engagement and job performance is moderated by the extent to which individuals are inclined to work hard, careful, and goal-oriented. On the basis of the literature, it was hypothesized that conscientiousness strengthens the relationship between work engagement and supervisor ratings of task and contextual performance as well as active learning. The hypotheses were tested on a sample of 144 employees from several occupations. Results of moderated structural equation modeling supported the hypotheses. Work engagement was positively related to task performance, contextual performance, and active learning, particularly for employees high in conscientiousness.
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Using Akaike's information criterion, three examples of statistical data are reanalyzed and show reasonably definite conclusions. One is concerned with the multiple comparison problem for the means in normal populations. The second is concerned with the grouping of the categories in a contingency table. The third is concerned with the multiple comparison problem for the analysis of variance by the iogit model in contingency tables, Finite correction of Akaike's information criterionis also proposed.
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This study focused on determining the relationships among student characteristics, such as gender, learning style, and varying prior computer experiences, and students’ linear and non-linear navigation of a hypermedia program. Additionally, the navigation patterns were analyzed at three different intervals to determine the relative temporal influence of these characteristics on linear and nonlinear navigation. It was found that authoring, programming, and gender (specifically female) were positively related to linear navigation during the early interval; that learning style (specifically field independent) and hypermedia experience were negatively related to linear navigation during the early interval; and that word processing experience, database experience, spreadsheet experience, learning style (specifically field independent), and hypermedia experience were positively related to nonlinear navigation during the early interval. At the middle interval, many of the characteristics were no longer distinguishing factors of linear or nonlinear behavior. Programming was still positively related to linear navigation; hypermedia experience, learning style (field independent), word processing experience, and database experience were negatively related to linear navigation. Hypermedia experience was the only factor having a relationship with nonlinear navigation; the relationship was positive. At the late interval, only years of computer experience and hypermedia were still distinguishing factors. Years of computer experience was positively related to linear navigation, and hypermedia was negatively related to linear navigation.
Article
Structural equation models (SEMs) make it possible to estimate the causal relationships, defined according to a theoretical model, linking two or more latent complex concepts, each measured through a number of observable indicators, usually called manifest variables. Traditionally, the component-based estimation of SEMs by means of partial least squares (PLS path modelling, PLS-PM) assumes homogeneity over the observed set of units: all units are supposed to be well represented by a unique model estimated on the overall data set. In many cases, however, it is reasonable to expect classes made of units showing heterogeneous behaviours to exist. Two different kinds of heterogeneity could be affecting the data: observed and unobserved heterogeneity. The first refers to the case of a priori existing classes, whereas in unobserved heterogeneity no information is available either on the number of classes or on their composition. If a group structure for the statistical units is given, the aim of the analysis is to search for any differences in the behaviours of the a priori given classes. In PLS-PM this would mean studying the effect of directly observed moderating variables, i.e. estimating as many (local) models as there are classes. Unobserved heterogeneity, instead, implies identifying classes of units (a priori unknown) having similar behaviours. Such heterogeneity is captured by an unobserved (latent) discrete moderating variable defining both the number of classes and the class membership. A new method for unobserved heterogeneity detection in PLS-PM is proposed in this paper: response-based procedure for detecting unit segments in PLS-PM (REBUS-PLS). REBUS-PLS, according to PLS-PM features, does not require distributional hypotheses and may lead to local models that are different in terms of both structural and measurement models. An application of REBUS-PLS on real data will be shown. Copyright