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Anagha Vaidya; Sarika Sharma – Interactive Technology and Smart Education, 2024
Purpose: Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation…
Descriptors: Course Evaluation, Learning Analytics, Formative Evaluation, Information Retrieval
Wenlong Yi; Xuan Huang; Sergey Kuzmin; Igor Gerasimov; Yun Luo – Education and Information Technologies, 2025
This study proposes a knowledge graph-based big data analysis model for course quality evaluation, aiming to address issues in online education course evaluations such as semantic bias, grammatical deficiencies, vocabulary limitations, false evaluations, information distortion, and imbalanced evaluation categories. The model incorporates three…
Descriptors: Electronic Learning, Online Courses, Course Evaluation, Concept Mapping
Xieling Chen; Di Zou; Haoran Xie; Gary Cheng; Zongxi Li; Fu Lee Wang – International Review of Research in Open and Distributed Learning, 2025
Massive open online courses (MOOCs) offer rich opportunities to comprehend learners' learning experiences by examining their self-generated course evaluation content. This study investigated the effectiveness of fine-tuned BERT models for the automated classification of topics in online course reviews and explored the variations of these topics…
Descriptors: MOOCs, Distance Education, Online Courses, Course Evaluation
Xieling Chen; Haoran Xie; Di Zou; Lingling Xu; Fu Lee Wang – Educational Technology & Society, 2025
In massive open online course (MOOC) environments, computer-based analysis of course reviews enables instructors and course designers to develop intervention strategies and improve instruction to support learners' learning. This study aimed to automatically and effectively identify learners' concerned topics within their written reviews. First, we…
Descriptors: Classification, MOOCs, Teaching Skills, Artificial Intelligence
Lewis, Norman P. – Journalism and Mass Communication Educator, 2021
A thematic evaluation of data journalism courses resulted in a typology that parses the field and offers guidance to educators. At the center is pattern detection, preceded by data acquisition and cleaning, and followed by data representation. The typology advances academic understanding by offering a precise conceptualization that distinguishes…
Descriptors: Data Analysis, Journalism Education, Classification, Audiences
Li, Yuheng; Rakovic, Mladen; Poh, Boon Xin; Gaševic, Dragan; Chen, Guanliang – International Educational Data Mining Society, 2022
Learning objectives, especially those well defined by applying Bloom's taxonomy for Cognitive Objectives, have been widely recognized as important in various teaching and learning practices. However, many educators have difficulties developing learning objectives appropriate to the levels in Bloom's taxonomy, as they need to consider the…
Descriptors: Educational Objectives, Taxonomy, Universities, Cognitive Ability
Wanxue Zhang; Lingling Meng; Bilan Liang – Interactive Learning Environments, 2023
With the continuous development of education, personalized learning has attracted great attention. How to evaluate students' learning effects has become increasingly important. In information technology courses, the traditional academic evaluation focuses on the student's learning outcomes, such as "scores" or "right/wrong,"…
Descriptors: Information Technology, Computer Science Education, High School Students, Scoring
Kazanidis, Ioannis; Theodosiou, Theodosios; Petasakis, Ioannis; Valsamidis, Stavros – Interactive Learning Environments, 2016
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…
Descriptors: Foreign Countries, Online Courses, Course Evaluation, Electronic Learning
Caird, Sally; Lane, Andy; Swithenby, Ed; Roy, Robin; Potter, Stephen – International Journal of Sustainability in Higher Education, 2015
Purpose: This research aims to examine the main findings of the SusTEACH study of the carbon-based environmental impacts of 30 higher education (HE) courses in 15 UK institutions, based on an analysis of the likely energy consumption and carbon emissions of a range of face-to-face, distance, online and information and communication technology…
Descriptors: Higher Education, Energy, Energy Conservation, Environmental Education
Romrell, Danae; Kidder, Lisa C.; Wood, Emma – Journal of Asynchronous Learning Networks, 2014
As mobile devices become more prominent in the lives of students, the use of mobile devices has the potential to transform learning. Mobile learning, or mLearning, is defined as learning that is personalized, situated, and connected through the use of a mobile device. As mLearning activities are developed, there is a need for a framework within…
Descriptors: Models, Evaluation Methods, Course Evaluation, Electronic Learning
Mazouz, Abdelkader; Crane, Keenan – Journal of Education and Learning, 2013
Establishing a link between Course Learning Outcomes (LOs) and Program Outcomes (POs) while assessing the course contents and delivery are among the most challenging issues in Higher Education. In the present study two forms were generated based on specific Course Learning Outcomes identified in the syllabus at the beginning of the teaching term:…
Descriptors: Course Evaluation, Outcomes of Education, Matrices, Course Content
Akhavan, Peyman; Arefi, Majid Feyz – Interdisciplinary Journal of E-Learning and Learning Objects, 2014
The purpose of this study is to obtain suitable quality criteria for evaluation of electronic content for virtual courses. We attempt to find the aspects which are important in developing e-content for virtual courses and to determine the criteria we need to judge for the quality and efficiency of learning objects and e-content. So we can classify…
Descriptors: Electronic Learning, Classification, Educational Technology, Course Content
Wolbring, Tobias – Evaluation Review, 2012
Background: Many university departments use students' evaluations of teaching (SET) to compare and rank courses. However, absenteeism from class is often nonrandom and, therefore, SET for different courses might not be comparable. Objective: The present study aims to answer two questions. Are SET positively biased due to absenteeism? Do…
Descriptors: Research Design, Teacher Effectiveness, Student Evaluation of Teacher Performance, Attendance
Tickell, Geoffrey; Lim, Tiong Kiong; Balachandran, Balasinghan – American Journal of Business Education, 2012
This paper contributes to the continuing debate regarding the curriculum for the first undergraduate course in accounting by examining student perceptions from studying such a course. Participants are divided into two cohorts--Accounting & Finance Majors (AFM) and Other Business Majors (OBM). Results reported in this paper indicate that…
Descriptors: Majors (Students), Nonmajors, Student Attitudes, Accounting
O'Neill, Angie; Birol, Gülnur; Pollock, Carol – Canadian Journal for the Scholarship of Teaching and Learning, 2010
The objectives of this study were to investigate the alignment of exam questions with course learning outcomes in a first year biology majors course, to examine gaps and overlaps in assessment of content amongst the sections of the course, and to use this information to provide feedback to the teaching team to further improve the course. Our…
Descriptors: Foreign Countries, Biology, Science Instruction, Introductory Courses
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