Data Mining and Learning Analytics

Applications in Educational Research

Samira ElAtia, Donald Ipperciel, Osmar R. Zaïane, Samira ElAtia, Donald Ipperciel, Osmar R. Zaïane

• Publisher: Wiley
• Year: 2016
• Print ISBN: 9781118998236
• eBook ISBN: 9781118998212
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning

This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining's four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM's emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields.

• Includes case studies where data mining techniques have been effectively applied to advance teaching and learning
• Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students
• Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students
• Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics

Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

PART I
AT THE INTERSECTION OF TWO FIELDS: EDM

1. CHAPTER 1 EDUCATIONAL PROCESS MINING: A TUTORIAL AND CASE STUDY USING MOODLE DATA SETS
2. CHAPTER 2 ON BIG DATA AND TEXT MINING IN THE HUMANITIES
3. CHAPTER 3 FINDING PREDICTORS IN HIGHER EDUCATION
4. CHAPTER 4 EDUCATIONAL DATA MINING: A MOOC EXPERIENCE
5. CHAPTER 5 DATA MINING AND ACTION RESEARCH

CHAPTER 1
EDUCATIONAL PROCESS MINING: A TUTORIAL AND CASE STUDY USING MOODLE DATA SETS

Cristóbal Romero¹, Rebeca Cerezo², Alejandro Bogarín¹, and Miguel Sánchez‐Santillán²

¹ Department of Computer Science, University of Córdoba, Córdoba, Spain

² Department of Psychology, University of Oviedo, Oviedo, Spain

The use of learning management systems (LMSs) has grown exponentially in recent years, which has had a strong effect on educational research. An LMS stores all students’ activities and interactions in files and databases at a very low level of granularity (Romero, Ventura, & García, 2008). All this information can be analyzed in order to provide relevant knowledge for all stakeholders involved in the teaching–learning process (students, teachers, institutions, researchers, etc.). To do this, data mining (DM) can be used to extract information from a data set and transform it into an understandable structure for further use. In fact, one of the challenges that the DM research community faces is determining how to allow professionals, apart from computer scientists, to take advantage of this methodology. Nowadays, DM techniques are applied successfully in many areas, such as business marketing, bioinformatics, and education. In particular, the area that applies DM techniques in educational settings is called educational data mining (EDM). EDM deals with unintelligible, raw educational data, but one of the core goals of this discipline—and the present chapter—is to make this valuable data legible and usable to students as feedback, to professors as assessment, or to universities for strategy. EDM is broadly studied, and a reference tutorial was developed by Romero et al. (2008). In this tutorial, the authors show the step‐by‐step process for doing DM with Moodle data. They describe how to apply preprocessing and traditional DM techniques (such as statistics, visualization, classification, clustering, and association rule mining) to LMS data.

One of the techniques used in EDM is process mining (PM). PM starts from data but is process centric; it assumes a different type of data: events. PM is able to extract knowledge of the event log that is commonly available in current information systems. This technique provides new means to discover, monitor, and improve processes in a variety of application domains. The implementation of PM activities results in models of business processes and historical information (more frequent paths, activities less frequently performed, etc.). Educational process mining (EPM) involves the analysis and discovery of processes and flows in the event logs generated by educational environments. EPM aims to build complete and compact educational process models that are able to reproduce all the observed behaviors, check to see if the modeling behavior matches the behavior observed, and project extracted information from the registrations in the pattern to make the tacit knowledge explicit and to facilitate a better understanding of the process (Trcka & Pechenizkiy, 2009).

EPM has been previously applied successfully to the educational field; one of the most promising applications is used to study the difficulties that students of different ages show when learning in highly cognitively and metacognitively demanding learning environments, such as a hypermedia learning environment (Azevedo et al., 2012). These studies describe suppositions and commonalities across several of the foremost EPM models for self‐regulated learning (SRL) with student‐centered learning environments (SCLEs). It supplies examples and definitions of the key metacognitive monitoring processes and the regulatory skills used when learning with SCLEs. It also explains the assumptions and components of a leading information processing model of SRL and provides specific examples of how EPM models of metacognition and SRL are embodied in four current SCLEs.

However, several problems have been previously found when using EPM (Bogarín et al., 2014). For instance, the model obtained is not well adjusted to the general behavior of students, and the resulting model may be too large and complex for a teacher or student to analyze. In order to solve these problems, we propose the use of clustering for preprocessing the data before applying EPM to improve understanding of the obtained models. Clustering techniques divide complex phenomena—described by sets of objects or by highly dimensional data—into small, comprehensible groups that allow better control and understanding of information. In this work, we apply clustering as a preprocessing task for grouping users based on their type of course interactions. Thus, we expect to discover the most specific browsing behaviors when using only the clustered data rather than the full data set. This chapter describes, in a practical tutorial, how to apply clustering and EPM to Moodle data using two well‐known open‐source tools: Weka (Witten, Frank, & Hall, 2011) and ProM (Van der Aalst, 2011a).

The chapter is organized as follows: Section 1.1 describes the most relevant works related to the chapter, Section 1.2 describes the data preparation and clustering, Section 1.3 describes the application of PM, and Section 1.4 outlines some conclusions and suggestions for further research.

1.1 BACKGROUND

Process mining (PM) is a data mining (DM) technique that uses event logs recorded by systems in order to discover, monitor, and improve processes in different domains. PM is focused on processes, but it also uses the real data (Van der Aalst, 2011a). It is the missing link between the classical process model of analysis and data‐oriented analysis like DM and machine learning. We can think of PM as a bridge between processes and data, between business process management and business intelligence, and between compliance and performance. PM connects many different ideas, and that makes it extremely valuable (Van der Aalst, 2011b).

The starting point for PM is event data. We assume that there is an event log in which each event refers to a case, an activity, and a point in time or time stamp. An event log can be seen as a collection of cases (which we sometimes also refer to as traces); each case corresponds to a sequence of events. Event data comes from a large variety of sources. PM consists of different types of mining (Van der Aalst et al., 2012):

• Process discovery conforms to a model.
• Conformance checking is a form of replay aimed at finding deviations.
• Enhancement is also a form of replay with the goal of finding problems (such as bottlenecks) or ideas for improvement.

The potential and challenges of PM have been previously investigated in the field of professional training (Cairns et al., 2014). For instance, this field has focused on the mining and analysis of social networks involving course units or training providers; it has also proposed a two‐step clustering approach for partitioning educational processes following key performance indicators. Sedrakyan, Snoeck, and De Weerdt (2014) attempted to obtain empirically validated results for conceptual modeling of observations of activities in an educational context. They tried to observe the characteristics of the modeling process itself, which can be associated with better/worse learning outcomes. In addition, the study provided the first insights for learning analytics research in the domain of conceptual modeling.

The purpose o...

Table of contents

• COVER
• TITLE PAGE
• TABLE OF CONTENTS
• NOTES ON CONTRIBUTORS
• INTRODUCTION
• PART I: AT THE INTERSECTION OF TWO FIELDS: EDM
• PART II: PEDAGOGICAL APPLICATIONS OF EDM
• PART III: EDM AND EDUCATIONAL RESEARCH
• INDEX
• END USER LICENSE AGREEMENT