Peer assessment in MOOCs: Systematic literature review

Gamage, Dilrukshi; Staubitz, Thomas; Whiting, Mark E.

doi:10.1080/01587919.2021.1911626

Cited by 28 publications

(10 citation statements)

References 53 publications

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“…The sheer number of students enrolled in MOOCs makes it impossible to grade assignments and examinations manually. Instructors use instruments that enable automated grading to assess the performance of participants [15] A blended model was developed for the characteristics of interaction, learning resources, and learning technique; as a result, the levels of the three characteristics contained the term "mainly or primarily," for instance, "largely asynchronous interaction vs. synchronous interaction." This held for the characteristics of learning resources and the learning approach [16].…”

Section: A Online Courses and Mooc Platformsmentioning

confidence: 99%

E-Learning Pricing Model Policy for Higher Education

et al. 2023

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The technological advancement in learning has made it possible for students to study beyond space and time restrictions, known as online learning. Impacted by the pandemic for an extended period, most students have adapted to online learning and, even more, have realized the vast benefits of online learning despite all the minor disadvantages. As a result, worldwide institutions, including Indonesia, are now offering online degrees and courses. Previous studies have shown contradictory results of cost factor effects on online courses, from the least important to the most critical factor for student achievements. Therefore, deciding the online course rates has been a major concern for online course providers. This research aims to answer the fundamental question of designing costs for online learning by analyzing online course preferences and ensuring sustainability by proposing a framework for the E-learning Pricing Model Policy in Higher Education using literature studies and qualitative approaches. The results show four main phases: the preparation phase, which conducts market research to understand consumer demand and behavior; the implementation phase, which includes marketing expenses and tutor fees; evaluation phase, which includes the course content material and video production revisions for further implementation. In addition, the infrastructure phase as the Learning Management System's virtual space is added with the Cloud Expenses. However, as a limitation of this research, countless factors influence online course rates, and no exact number can determine those rates. Nevertheless, the course cost can be estimated by considering the factors that affect the overall cost and the number of learners who take the course. As a result, this framework acts as an essential foundation for institutions to determine sustainable online course rates.

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Section: A Online Courses and Mooc Platformsmentioning

confidence: 99%

E-Learning Pricing Model Policy for Higher Education

et al. 2023

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“…Finally, in terms of the basic need for perceived competence supported by teachers, each learning task assigned by teachers should be linked to specific training objectives and purposes. Tasks should also adopt peer assessment accompanied by criteria to achieve learning results (Huisman et al, 2018;Gamage et al, 2021).…”

Section: Practical Implicationsmentioning

confidence: 99%

Students’ persistence intention in MOOCs in the psychomotor domain: An extended 3P model of the teaching and learning perspective

Yang

Lin²

2023

Front. Psychol.

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This study proposed and tested a model adapted from Biggs’ 3P model that quantifies the behaviors of students who completed MOOCs (Massive Open Online Courses) in order to design intervention measures for low retention rates. Psychomotor domain data from 300 MOOC learners was analyzed in a covariance-based structural equation model (CB-SEM) to analyze the direct and indirect effects of various factors. Results show the basic psychological needs theory (BPNT) in the presage stage significantly positively correlated with engagement in the process stage. Meanwhile, the process stage exhibited a significantly positive correlation with the product stage, representing persistence intention (PI). Furthermore, a full mediation effect was observed among the presage, process, and product stages. The mediating effect demonstrates that higher student engagement leads to more positive exertion on BNPT and PI to complete the course. Moreover, results show bolstering students’ behavioral, emotional, and cognitive engagement strengthens their PIs.

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“…Some researchers examined the potential applications of these courses in a variety of academic subjects [14]. Prior studies have also examined the assessment challenges associated with these courses [15]. Scholars have also focused their attention on learning theories and pedagogical approaches for MOOCs [16].…”

Section: Massive Online Open Courses (Moocs) Adoptionmentioning

confidence: 99%

Influencing Factors in MOOCs Adoption in Higher Education: A Meta-Analytic Path Analysis

et al. 2022

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(1) Background: Due to the rapid growth of Massive Online Open Courses (MOOCs), higher educational institutions across the world are investing heavily in MOOCs to support their traditional teaching, their students’ learning experience, and their performance. However, the success of MOOCs highly depends on several factors that influence their success in higher education. Prior studies have attempted to investigate and predict user acceptance of MOOCs in higher education by using a variety of theoretical viewpoints. Nonetheless, these studies have yielded conflicting findings and are inconclusive. (2) Purpose: This study aims to develop a model that integrates the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), as well as the Task-Technology Fit (TTF) to explore the factors that influence the acceptance and use of MOOCs in higher education institutions, while synthesizing previous empirical findings in the field. (3) Methods: The model was tested using Meta-analytic Structural Equation Modelling (MASEM) based on the data gathered from 43 studies (k = 45 samples, n = 16,774). (4) Results: Effort expectancy (EE), attitude (ATT), performance expectancy (PE), and TTF—determined by several task and technology characteristics—were identified as the direct predictors of behavioral intention (BI) to continue using MOOCs. (5) Conclusions: This model provides a cohesive view of MOOCs’ acceptance in higher educational institutions, and it helps to identify potential research opportunities in this area. (6) Implications: Results from MASEM offer managerial guidance for the effective implementation of MOOCs and provide directions for further research, to augment current knowledge of MOOCs’ adoption, by higher education institutions.

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Peer assessment in MOOCs: Systematic literature review

Cited by 28 publications

References 53 publications

E-Learning Pricing Model Policy for Higher Education

E-Learning Pricing Model Policy for Higher Education

Students’ persistence intention in MOOCs in the psychomotor domain: An extended 3P model of the teaching and learning perspective

Influencing Factors in MOOCs Adoption in Higher Education: A Meta-Analytic Path Analysis

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