Vitomir Kovanović

Penetrating the black box of time-on-task estimation

All forms of learning take time. There is a large body of research suggesting that the amount of time spent on learning can improve the quality of learning, as represented by academic performance. The wide-spread adoption of learning technologies such as learning management systems (LMSs), has resulted in large amounts of data about student learning being readily accessible to educational researchers. One common use of this data is to measure time that students have spent on different learning tasks (i.e., time-on-task). Given that LMS systems typically only capture times when students executed various actions, time-on-task measures are estimated based on the recorded trace data. LMS trace data has been extensively used in many studies in the field of learning analytics, yet the problem of time-on-task estimation is rarely described in detail and the consequences that it entails are not fully examined. This paper presents the results of a study that examined the effects of different time-on-task estimation methods on the results of commonly adopted analytical models. The primary goal of this paper is to raise awareness of the issue of accuracy and appropriateness surrounding time-estimation within the broader learning analytics community, and to initiate a debate about the challenges of this process. Furthermore, the paper provides an overview of time-on-task estimation methods in educational and related research fields.

Learning at distance

Contemporary literature on online and distance education almost unequivocally argues for the importance of interactions in online learning settings. Nevertheless, the relationship between different types of interactions and learning outcomes is rather complex. Analyzing 204 offerings of 29 courses, over the period of six years, this study aimed at expanding the current understanding of the nature of this relationship. Specifically, with the use of trace data about interactions and utilizing the multilevel linear mixed modeling techniques, the study examined whether frequency and duration of student-student, student-instructor, student-system, and student-content interactions had an effect of learning outcomes, measured as final course grades. The findings show that the time spent on student-system interactions had a consistent and positive effect on the learning outcome, while the quantity of student-content interactions was negatively associated with the final course grades. The study also showed the importance of the educational level and the context of individual courses for the interaction types supported. Our findings further confirmed the potential of the use of trace data and learning analytics for studying learning and teaching in online settings. However, further research should account for various qualitative aspects of the interactions used while learning, different pedagogical/media features, as well as for the course design and delivery conditions in order to better explain the association between interaction types and the learning achievement. Finally, the results might imply the need for the development of the institutional and program-level strategies for learning and teaching that would promote effective pedagogical approaches to designing and guiding interactions in online and distance learning settings. We examined the relationship between interaction types and learning out-come.The findings show significant positive effect of student-system interactions.Student-content interactions were negatively associated with the learning outcome.Educational level and course context are important for interaction types supported.

Tools for Educational Data Mining A Review

In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.

Towards automated content analysis of discussion transcripts: a cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohens kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

Profiling MOOC Course Returners: How Does Student Behavior Change Between Two Course Enrollments?

Massive Open Online Courses represent a fertile ground for examining student behavior. However, due to their openness MOOC attract a diverse body of students, for the most part, unknown to the course instructors. However, a certain number of students enroll in the same course multiple times, and there are records of their previous learning activities which might provide some useful information to course organizers before the start of the course. In this study, we examined how student behavior changes between subsequent course offerings. We identified profiles of returning students and also interesting changes in their behavior between two enrollments to the same course. Results and their implications are further discussed.

Piecing the learning analytics puzzle: a consolidated model of a field of research and practice

ABSTRACT The field of learning analytics was founded with the goal to harness vast amounts of data about learning collected by the extensive use of technology. After the early formation, the field has now entered the next phase of maturation with a growing community who has an evident impact on research, practice, policy, and decision-making. Although learning analytics is a bricolage field borrowing from many related other disciplines, there is still no systematised model that shows how these different disciplines are pieced together. Existing models and frameworks of learning analytics are valuable in identifying elements and processes of learning analytics, but they insufficiently elaborate on the links with foundational disciplines. With this in mind, this paper proposes a consolidated model of the field of research and practice that is composed of three mutually connected dimensions – theory, design, and data science. The paper defines why and how each of the three dimensions along with their mutual relations is critical for research and practice of learning analytics. Finally, the paper stresses the importance of multi-perspective approaches to learning analytics based on its three core dimensions for a healthy development of the field and a sustainable impact on research and practice.

Exploring communities of inquiry in Massive Open Online Courses

Abstract This study presents an evaluation of the Community of Inquiry (CoI) survey instrument developed by Arbaugh et al. (2008) within the context of Massive Open Online Courses (MOOCs). The study reports the results of a reliability analysis and exploratory factor analysis of the CoI survey instrument using the data of 1487 students from five MOOC courses. The findings confirmed the reliability and validity of the CoI survey instrument for the assessment of the key dimensions of the CoI model: teaching presence, social presence, and cognitive presence. Although the CoI survey instrument captured the same latent constructs within the MOOC context as in the Garrisons three-factor model (Garrison et al., 1999), analyses suggested a six-factor model with additional three factors as a better fit to the data. These additional factors were 1) course organization and design (a sub-component of teaching presence), 2) group affectivity (a sub-component of social presence), and 3) resolution phase of inquiry learning (a sub-component of cognitive presence). The emergence of these additional factors revealed that the discrepancies between the dynamics of the traditional online courses and MOOCs affect the student perceptions of the three CoI presences. Based on the results of our analysis, we provide an update to the famous CoI model which captures the distinctive characteristics of the CoI model within the MOOC setting. The results of the study and their implications are further discussed.

How Do We Model Learning at Scale? A Systematic Review of Research on MOOCs:

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models.

Introduction to data mining for educational researchers

The goal of this tutorial is to share data mining tools and techniques used by computer scientists with educational social scientists. We broadly define educational social scientists as being made up of people with backgrounds in the learning sciences, cognitive psychology, and educational research. The learning analytics community is heavily populated with researchers of these backgrounds, and we believe those that find themselves at the intersection of research, theory, and practice have a particular interest in expanding their knowledge of datadriven tools and techniques.

Highway: a domain specific language for enterprise application integration

Highway is a domain-specific language for implementing enterprise application integration solutions in a technology independent and functional manner. As an internal DSL developed on top of Clojure programming language, Highway uses functional programming techniques in order to simplify enterprise application integration development. In this paper we focus on abstractions and language constructs that define Highways approach to integration. We also cover implementation of enterprise integration patterns using Highway since they represent various common situations in enterprise application integration development.