Evaluation Of Massive Open Online Courses Research Articles

Towards Quality Assurance in MOOCs: A Comprehensive Review and Micro-Level Framework

MOOCs (massive open online courses), because of their scale and accessibility, have become a major area of interest in contemporary education. However, despite their growing popularity, the question of their quality remains a central concern, partly due to the lack of consensus on the criteria establishing such quality. This study set out to fill this gap by carrying out a systematic review of the existing literature on MOOC quality and proposing a specific quality assurance framework at a micro level. The methodology employed in this research consisted of a careful analysis of MOOC success factor’s using Biggs’ classification scheme, conducted over a four-year period from 2018 to 2022. The results highlighted the compelling need to consider various indicators across presage, process, and product variables when designing and evaluating MOOCs. This implied paying particular attention to pedagogical quality, both from the learner’s and the teacher’s point of view. The quality framework thus developed is of significant importance. It offers valuable guidance to MOOC designers, learners, and researchers, providing them with an in-depth understanding of the key elements contributing to MOOC quality and facilitating their continuous improvement. In addition, this study highlighted the need to address aspects for future research, including large-scale automated evaluation of MOOCs. By focusing on pedagogical quality, MOOCs can play a vital role in providing meaningful learning experiences, maximizing learner satisfaction, and ensuring their success as innovative educational systems adapted to the changing needs of contemporary education.

Where and what to improve? Design and application of a MOOC evaluation framework based on effective teaching practices

Given the varied quality of massive open online courses (MOOCs), it is necessary to construct a MOOC evaluation framework to guide teachers’ online teaching practices. Based on the Quality Matters online course evaluation framework and studies on effective online teaching practices, a MOOC evaluation framework was constructed through literature analysis and expert interviews. Finally, an evaluation framework with eight dimensions and 42 secondary indicators was constructed. To examine its validity, five MOOCs were evaluated with this framework. Results show that the evaluation framework is helpful for evaluating MOOCs and providing implications for online instructors. They revealed the common limitations of MOOCs: single method for knowledge introduction, traditional evaluation methods, insufficient attention to student initiative, and inadequate copyright concerns. Corresponding suggestions are provided to improve the quality of MOOCs. Theoretical and practical implications for the improvement of MOOCs are discussed along with avenues for further research.

MOOC Evaluation System Based on Deep Learning

Massive open online courses (MOOCs) are open access, Web-based courses that enroll thousands of students. MOOCs deliver content through recorded video lectures, online readings, assessments, and both student–student and student–instructor interactions. Course designers have attempted to evaluate the experiences of MOOC participants, though due to large class sizes, have had difficulty tracking and analyzing the online actions and interactions of students. Within the broader context of the discourse surrounding big data, educational providers are increasingly collecting, analyzing, and utilizing student information. Additionally, big data and artificial intelligence (AI) technology have been applied to better understand students’ learning processes. Questionnaire response rates are also too low for MOOCs to be credibly evaluated. This study explored the use of deep learning techniques to assess MOOC student experiences. We analyzed students’ learning behavior and constructed a deep learning model that predicted student course satisfaction scores. The results indicated that this approach yielded reliable predictions. In conclusion, our system can accurately predict student satisfaction even when questionnaire response rates are low. Accordingly, teachers could use this system to better understand student satisfaction both during and after the course.

A MOOC for Farmers: Agroclimatic Tools for Crop Protection

Massive open online courses (MOOCs) are a key strategy for digital education. The MOOCs contribute significantly to people’s knowledge about a wide range of topics. Nowadays, several web platforms as Coursera and edX offer MOOCs in different domains, however the platforms mentioned do not offer MOOCs focused on crop management through monitoring of climate elements and factors. In this paper, we present an overview of the MOOC titled: “agroclimatic tools for crop protection” for agricultural-sector Spanish speakers. We show a first MOOC evaluation based on a survey applied to 13 people of rural areas located in Cauca (Colombia) for one video of the “Temperature” MOOC unit. The results indicated that 100 % of the respondents understood clearly the video content and 53, 84 % of the surveyed learners understood all the words used in the video.

Design and validation of a diagnostic MOOC evaluation method combining AHP and text mining algorithms

ABSTRACT 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 integrate expert opinions, standardized rubrics, and learner feedback into the evaluation process. For this purpose, 30 MOOCs from the Coursera website were purposively selected and evaluated using the DME method and the results compared with expert evaluation and learner rating scores. The preliminary findings, in general, support the feasibility, accuracy, and diagnostic utility of the DME method and its suitability as a low-cost, sophisticated, and accurate method for MOOC evaluation. Finally, the study discusses several limitations and technical issues of the DME method that should be addressed in future research and practice.

Massive Open Online Courses (MOOC) Evaluation Methods: Protocol for a Systematic Review.

BackgroundMassive open online courses (MOOCs) have increased in popularity in recent years. They target a wide variety of learners and use novel teaching approaches, yet often exhibit low completion rates (10%). It is important to evaluate MOOCs to determine their impact and effectiveness, but little is known at this point about the methodologies that should be used for evaluation.ObjectiveThe purpose of this paper is to provide a protocol for a systematic review on MOOC evaluation methods.MethodsWe will use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines for reporting this protocol. We developed a population, intervention, comparator, and outcome (PICO) framework to guide the search strategy, based on the overarching question, “What methods have been used to evaluate MOOCs?” The review will follow six stages: 1) literature search, 2) article selection, 3) data extraction, 4) quality appraisal, 5) data analysis, and 6) data synthesis.ResultsThe systematic review is ongoing. We completed the data searches and data abstraction in October and November 2018. We are now analyzing the data and expect to complete the systematic review by March 2019.ConclusionsThis systematic review will provide a useful summary of the methods used for evaluation of MOOCs and the strengths and limitations of each approach. It will also identify gaps in the literature and areas for future work.International Registered Report Identifier (IRRID)DERR1-10.2196/12087

MOOC Platforms: a Review and Comparison

For development courses in the Massive Open Online Courses (MOOC) requires the support of good technological infrastructure. Some MOOC platforms are part of the institution but some platforms are built by individual teachers or instructors. The objective of this study to reviews about characteristics in MOOC platforms and evaluation of comparison based on previous studies with specific in MOOC platforms. The databases in MOOC studies found in four databases namely Google Scholar, IEEExplore, Elsevier Science Direct, and Emerald. After the databases filtered, 20 papers in MOOC studies that selected and 4 databases related in development Open Learning and Udemy platforms. This study aims to bring the knowledge for the developer or research groups about MOOC platforms and as a consideration to choose which one suitable platforms to create, manage, and deliver courses.

Real-world evidence for postgraduate students and professionals in healthcare: protocol for the design of a blended massive open online course

IntroductionThere is an increased need for improving data science skills of healthcare professionals. Massive open online courses (MOOCs) provide the opportunity to train professionals in a sustainable and cost-effective way....

Protocol for a mixed-methods evaluation of a massive open online course on real world evidence

IntroductionIncreasing number of Massive Open Online Courses (MOOCs) are being used to train learners at scale in various healthcare-related skills. However, many challenges in course delivery require further understanding, for...

Design, implementation and evaluation of MOOCs and DBL‐based cross‐registration

AbstractTo overcome geographical restrictions of the cross‐registration, this study uses MOOCs and DBL as the learning approach to presents an implementation process of cross‐registration in the course of Machinery Design. Cross‐registration contains three steps: Pre‐Registration, Learning Process, and Credit Certification. According to the characteristics of cross‐registration, Learning process was realized through the mode which consists of Massive Open Online Courses (MOOCs) learning, traditional classroom and Design Based Learning (DBL). Project progression of DBL was elaborated, demonstrated and had been applied in cross‐registration. This work aims at analyzing and reporting whether this MOOCs and DBL‐based cross‐registration has effects on a case study in Machinery Design Course. Students’ performance and learning behaviors are analyzed in this article. The results indicate that the MOOCs‐based cross‐registration is practicable, Learning Process has positive influence on students' performance. Recommendations for future work and implications for practice will also be discussed.

Lessons Learned from the Dying2Learn MOOC: Pedagogy, Platforms and Partnerships

(1) Background: Massive Open Online Courses (MOOCs) are becoming more commonplace in the delivery of free online education and a Dying2Learn MOOC was offered by a team at Palliative and Supportive Services, Flinders University, South Australia; (2) Methods: Working with the OpenLearning platform developer, a research study and MOOC evaluation were embedded in the course, and content was delivered in innovative ways without compromising pedagogical approaches; (3) Results: This MOOC provided the facilitators with the opportunity to view education as an intervention, with testing undertaken, including measuring attitudinal change. Research, clinical and community partnerships were developed or reaffirmed and the value of ongoing partnerships with developers in creating platforms and tools that can expand the options for online learning is highlighted. Opportunities for future health professional and consumer education were also explored; (4) Conclusion: MOOCs can provide innovative opportunities to redesign educational approaches, which can be achieved by working with new technologies and with platform developers, while still adhering to pedagogical principles.

Measuring the success of scaleable open online courses

Purpose – The purpose of this paper is to propose measures of online open course success for non-commercial institutional providers of massive open online courses (MOOCs) and other scaleable open online courses (SOOCs). Design/methodology/approach – The measures are derived from the characteristics of open online courses, existing knowledge about open online course providers and users and their motivations, and current practice in MOOC evaluation and data analytics. Findings – Current practices for evaluation of open online courses are dominated by MOOC analytics which provide insights into user demographics and behaviour with some implications for evaluation of reach and course design but leaving many unknowns. Measures for evaluation of success at the institutional level can be derived from institutional goals for open online courses. Success from the point of view of teachers and technical teams involved in design, development and delivery of open online courses can be derived from team members’ expectations, resources and satisfaction as well as measures of cost and effort compared to budget and benchmarks. Users are classified as registrants (information seekers, window shoppers, samplers), downloaders and participants (starters, partial participants and full participants who are further divided into auditing, active and certificate takers); different measures are appropriate for each group. Practical implications – Practitioners and researchers must consider a variety of levels and indicators of success to adequately evaluate open online courses. Tables in the text propose measures, methods, timing and roles. Originality/value – This is the first published paper to take a holistic view of open online course evaluation and propose detailed measures.