Assessment Essentials: Planning, Implementing, and Improving Assessment in Higher Education

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.

Educational data mining and learning analytics: An updated survey

Predicting the students’ performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students’ performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually identified as educational data mining. Hence, the main aim of this study is to identify the most commonly studied factors that affect the students’ performance, as well as, the most common data mining techniques applied to identify these factors. In this study, 36 research articles out of a total of 420 from 2009 to 2018 were critically reviewed and analyzed by applying a systematic literature review approach. The results showed that the most common factors are grouped under four main categories, namely students’ previous grades and class performance, students’ e-Learning activity, students’ demographics, and students’ social information. Additionally, the results also indicated that the most common data mining techniques used to predict and classify students’ factors are decision trees, Naive Bayes classifiers, and artificial neural networks.

Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

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A Systematic Review of Deep Learning Approaches to Educational Data Mining

Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning. Although several EDM surveys are available in the literature, we could find only a few specific surveys on student performance analysis and prediction. These specific surveys are limited in nature and primarily focus on studies that try to identify possible predictor or model student performance. However, the previous works do not address the temporal aspect of prediction. Moreover, we could not find any such specific survey which focuses only on classroom-based education. In this paper, we present a systematic review of EDM studies on student performance in classroom learning. It focuses on identifying the predictors, methods used for such identification, time and aim of prediction. It is significantly the first systematic survey of EDM studies that consider only classroom learning and focuses on the temporal aspect as well. This paper presents a review of 140 studies in this area. The meta-analysis indicates that the researchers achieve significant prediction efficiency during the tenure of the course. However, performance prediction before course commencement needs special attention.

Student performance analysis and prediction in classroom learning: A review of educational data mining studies

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.

Educational data mining applications and tasks: A survey of the last 10 years

Assessing learning outcomes for students in higher education institutes is an interesting task with many potential applications for all involved stakeholders (students, administrators, potential employers, etc.). In this paper, we propose a course recommendation system for students based on the assessment of their "graduate attributes" (i.e. attributes that describe the developing values of students). Students rate the improvement in their graduating attributes after a course is finished and a collaborative filtering algorithm is utilized in order to suggest courses that were taken by fellow students and rated in a similar way. An extension to weigh the most recent ratings as more important is included in the algorithm which is shown to have better accuracy than the baseline approach. Experimental results using correlation thresholding and the nearest neighbors approach show that such a recommendation system can be effective when an active neighborhood of 10-15 students is used and show that the numbers of users used can be decreased effectively to one fourth of the whole population for improving the performance of the algorithm.

A Course Recommender System based on Graduating Attributes

In this paper, the challenging and thorny issue of assessing graduate attributes (GAs) is addressed. An interdisciplinary team at The University of Alberta ----developed a formative model of assessment centered on students and instructor interaction with course content.,The paper starts by laying the theoretical groundwork on which this novel GA assessment tool is based, that is, competency-based education, assessment theory and GA assessment. It follows with a description of the online assessment tool for GAs that was developed in the course of this project.,The online assessment tool for GAs targets three types of stakeholders: (1) students, who self-assess in terms of GAs, (2) instructors, who use the tool to define the extent to which each GA should be inculcated in their course and (3) administrators, who receive aggregate reports based on the data gathered by the system for high-level analysis and decision-making. Collected data by students and professors advance formative assessment of these transversal skills and assist administration in ensuring the GAs are addressed in academic programs. Graduate attributes assessment program (GAAP) is also a space for students to build a personal portfolio that would be beneficial to highlight their skills for potential employers.,This research has strong implications for the universities, since it can help institutions, academics and students achieve better results in their practices. This is done by demonstrating strong links between theory and practice. Although this tool has only been used within the university setting by students, instructors and administrators (for self-, course and teaching and program improvement), it could increase its social and practical impact by involving potential employers and increase our understanding of student employability. Moreover, because the tool collects data on a continuous basis, it lends itself to many possible applications in educational data mining,,The GAAP can be used and adapted to various educational contexts. The plugin can be added to any Learning Management System (LMS), and students can have access to their data and results throughout their education.,The GAAP allows institutions to provide a longitudinal formative assessment of students’ graduate attributes acquisition. It provides solid and valid evidence of students’ progress in a way that would advance society and citizenship.,To date, the GAAP is the first online interactive platform that has been developed to longitudinally assess the acquisition of GAs during a complete academic cycle/cohort. It provides a unique space where students and instructors interact with assessment scales and with concrete data for a complete university experience profile.

Graduate Attributes Assessment Program

Social networks have been popular since a few years ago. They still attract the attentions of developers to come up with new features. In this paper we have introduced an anonymous social network based on recommending shared items. We have tried to hide the record and status of user, from sights of others via anonymity. This social network designed to recommend both content and people in respect to user's ratings. In order to evaluate our proposed recommending algorithms, we introduced two metrics based on 6 months of online user study. These metrics have been used to evaluate desirability and relevancy of recommended items.

Expressmind: Recommending contents in an anonymous social network

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Behdad Bakhshinategh

Papers

Educational data mining applications and tasks: A survey of the last 10 years

A Course Recommender System based on Graduating Attributes

Graduate Attributes Assessment Program

Expressmind: Recommending contents in an anonymous social network

Design of a Course Recommender System as an Application of Collecting Graduating Attributes