The proliferation of massive open online courses (MOOCs) highlights the necessity of developing accurate and diagnostic evaluation methods to assess the courses' quality and effectiveness. Hence, this study proposes a diagnostic MOOC evaluation (DME) method that combines the Analytic Hierarchy Process algorithm and learner review mining to…

By using modern data analysis techniques, this study aims to construct an innovative university English teaching effectiveness evaluation model based on particle swarm algorithm and support vector machine. The model is designed to improve assessment accuracy and personalization. The research process includes the methodology of data collection,…

The purpose of this research is to examine the research that has been done on MOOCs by applying data mining and analytic approaches and to depict the current state of MOOC research. The text mining revealed four broad themes: (I) MOOCs as a mainstreaming learning model in HE, (II) motivation and engagement issues in MOOCs, (III) assessment issues…

Model evaluation -- the process of making inferences about the performance of predictive models -- is a critical component of predictive modelling research in learning analytics. We survey the state of the practice with respect to model evaluation in learning analytics, which overwhelmingly uses only naïve methods for model evaluation or…

This paper presents a case for the use of transaction-level data when analyzing automated online assessment results to identify knowledge gaps and misconceptions for individual students. Transaction-level data, which records all of the steps a student uses to complete an assessment item, are preferred over traditional assessment formats that…

Online learning has attracted a large number of participants and is increasingly becoming very popular. However, the completion rates for online learning are notoriously low. Further, unlike traditional education systems, teachers, if any, are unable to comprehensively evaluate the learning gain of each student through the online learning…

Massive Open Online Courses (MOOCs) are becoming an increasingly popular choice for education but, to reach their full extent, they require the resolution of new issues like assessing students at scale. A feasible approach to tackle this problem is peer assessment, in which students also play the role of assessor for assignments submitted by…

Big Data can radically transform education by enabling personalized learning, deep student modeling, and true longitudinal studies that compare changes across classrooms, regions, and years. With these promises, however, come risks to individual privacy and educational validity, along with deep policy and ethical issues. Education is largely a…

In this paper, we present an in-depth case study of a single student who failed an online module which formed part of a master's programme in Professional Education and Leadership. We use this case study to examine assessment practices in higher education in the online environment. In taking this approach, we go against the current predilection…

Many researchers who are interested in studying students' online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students' SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students' online…

Database files and additional log files of Learning Management Systems (LMSs) contain an enormous volume of data which usually remain unexploited. A new methodology is proposed in order to analyse these data both on the level of both the courses and the learners. Specifically, "regression analysis" is proposed as a first step in the…

In just a short period, social media have altered many aspects of our daily lives, from how we form and maintain social relationships to how we discover, access, and share information online. Now social media are also affecting how we teach and learn. In this paper, we discuss methods that can help researchers and educators evaluate and understand…

We present a method to help faculty assess and visualize conceptual knowledge by applying topic modeling to unstructured student writing from online class discussion forums. To validate the technique against conventional assessment metrics, we evaluated its accuracy in predicting final grades in introductory undergraduate biology and graduate…

Latent variable models, such as the popular Knowledge Tracing method, are often used to enable adaptive tutoring systems to personalize education. However, finding optimal model parameters is usually a difficult non-convex optimization problem when considering latent variable models. Prior work has reported that latent variable models obtained…

This descriptive study examines online assessment strategies employed by preservice teacher candidates when creating thematic learning experiences in online teaching environments. Using the learning management system Moodle as part of their innovative training, this review of a sample of 395 candidate-created instructional units across all grades…