Opportunities for Adopting Open Research Data in Learning Analytics

The relevance of open research data is already acknowledged in many disciplines. Demanded by publishers, funders, and research institutions, the number of published research data increases every day. In learning analytics though, it seems that data are not sufficiently published and re-used. This chapter discusses some of the progress that the learning analytics community has made in shifting towards open practices, and it addresses the barriers that researchers in this discipline have to face. As an introduction, the movement and the term open science is explained. The importance of its principles is demonstrated before the main focus is put on open data. The main emphasis though lies in the question, Why are the advantages of publishing research data not capitalized on in the field of learning analytics? What are the barriers? The authors evaluate them, investigate their causes, and consider some potential ways for development in the future in the form of a toolkit and guidelines.

Social Learning Analytics

Despite the idea that learning is individual, the YouTube channel has been examined to explain how learning is controlled in social networks. What kind of mechanism does the YouTube channel, which deeply influences the education world, work with and what does this structure tell the educators? What do data such as the number of views and comments on YouTube mean? Also, what kind of a model can be established between the video proposition system and our individual and social learning? This study has been prepared to shed light on the questions mentioned.

Educational Data Mining Techniques and Applications

Educational data mining (EDM) creates high impact in the field of academic domain. EDM is concerned with developing new methods to discover knowledge from educational and academic database and can be used for decision making in educational and academic systems. EDM is useful in many different areas including identifying at risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This chapter discusses educational data mining, its applications, and techniques that have to be adopted in order to successfully employ educational data mining and learning analytics for improving teaching and learning. The techniques and applications discussed in this chapter will provide a clear-cut idea to the educational data mining researchers to carry out their work in this field.

Learning Analytics as a Tool for Planning, Evaluating, and Improving a SMOOC for Teacher Professional Development

This chapter describes how a public university has met the challenge of changing inside the educational culture and preparing its teachers to manage online teaching-learning processes using learning analytics to contribute to the design, evaluation, and improvement of SMOOC. From the results obtained with a survey answered by the teachers, a twenty-hour class SMOOC was developed that focuses on instrumental aspects of digital technological resources in the teaching and learning contexts, as well as in the pedagogical issues, which concern the appropriate use of digital technologies. The findings of this research demonstrate that the SMOOC has been able to meet the teacher training needs, which are changing the way they work since remote education has been the viable alternative to guarantee the functioning of the university in the coronavirus pandemic period. However, it is believed that the knowledge that teachers are acquiring will contribute to changes in professional practice even post-pandemic.

Learning Analytics in the Monitoring of Learning Processes

During the last few years, learning analytics (LA) has gained the interest of researchers in the field of education. Generally, LA is related with the development of methods that use educational data sets to support the learning process. Therefore, there is a need understanding how learners, educators, and institutions can best support this process. Thus, a framework is presented that tries to extend the collaborative three-dimensional virtual environments for educational by integrating a LA tool. The aim is to help the teacher to monitor and evaluate the students' learning process in these types of environments. It is the intention include a (1) comprehensive analysis of the currently available LA tools for educational, (2) design of a user-centered framework based the requirements gathered from the analysis, and (3) thorough evaluation of framework to allow identify possible behavior patterns of students within the environment, related to your preferences for materials and expertise.

Big Data Analysis

The meaning of the term “big data” can be inferred by its name itself (i.e., the collection of large structured or unstructured data sets). In addition to their huge quantity, these data sets are so complex that they cannot be analyzed in any way using the conventional data handling software and hardware tools. If processed judiciously, big data can prove to be a huge advantage for the industries using it. Due to its usefulness, studies are being conducted to create methods to handle the big data. Knowledge extraction from big data is very important. Other than this, there is no purpose for accumulating such volumes of data. Cloud computing is a powerful tool which provides a platform for the storage and computation of massive amounts of data.

Learning Analytics and Education Data Mining in Higher Education

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.

The Practice of Gaining Universal Values Based on Learning Analytics Framework

This chapter encapsulates the framework of learning analytics. It is aimed to evaluate leisure activities and practices of students in learning outcomes based on the role of learning analytics framework. Qualitative research method was employed, and action research was conducted through activities to the 33 students. Data was analyzed based on content analysis. Metaphoric understanding and analysis of perceptions on activities were evaluated within the framework of learning analytics. It is seen that students felt the value of practice-based activities, and they become satisfied in their leisure times with different activities. In-service training is essential for teachers to develop the practice of leisure education.

Ethics in Predictive Learning Analytics

Most universities collect large amounts of students' data to enhance teaching, understand student behaviour, and predict their success. However, such practices raise privacy and ethical issues due to sensitive data harvesting practices. Despite the recognised importance of this topic, few empirical studies address how students perceive the ethical issues related to predictive learning analytics (PLA). To redress this, interview data collected from 42 undergraduate and postgraduate students in a Northern Irish university were thematically analysed. Findings suggest that there are at least three distinct groups of students having varying assumptions about ethics in PLA. They are (1) naïve and trusting, (2) cautious and compromising, and (3) enlightened and demanding, and all of them tend to narrowly focus only on the issue of informed consent. An empirically supported argument for the need for PLA researchers to recognise the within-group variations in student populations and to educate all types of students in issues related to ethics is presented.

Current State of Learning Analytics

Applying learning analytics (LAs) to actual teaching scenarios is a huge challenge. One of the problems that is required to be solved is how to combine LAs with pedagogy. Activity theory (AT) provides a conceptional tool for social human activities including objects and tools. Combining AT and pedagogical strategies as an analysis framework, this chapter analyzes LA application scenarios in seven components: subject, objective, community, tools, rules, division of labor, and outcomes. And learning theories present an in-depth analysis of rules. Conclusion shows in the LA application: teachers and students are main subjects; knowledge mastery is a common object; researchers and administrators play important roles while teachers have no specific teaching guidance to follow; presentation strategies of content are abundant; LAs integrate with multiple assessments; behaviorism, cognitivism, and constructivism embodied at different degrees; measurement of LAs application are diverse; not only learners, but characteristics of tasks need to be further studied.