scholarly journals Computer-Assisted Reading and Discovery for Student Generated Text in Massive Open Online Courses

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Justin Reich ◽  
Dustin Tingley ◽  
Jetson Leder-Luis ◽  
Margaret E. Roberts ◽  
Brandon Stewart
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Topic Model ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Course Evaluations ◽  
Computer Assisted ◽  
Discussion Forums ◽  
Massive Open Online ◽  
Daunting Challenge

Dealing with the vast quantities of text that students generate in Massive Open Online Courses (MOOCs) and other large-scale online learning environments is a daunting challenge. Computational tools are needed to help instructional teams uncover themes and patterns as students write in forums, assignments, and surveys. This paper introduces to the learning analytics community the Structural Topic Model, an approach to language processing that can 1) find syntactic patterns with semantic meaning in unstructured text, 2) identify variation in those patterns across covariates, and 3) uncover archetypal texts that exemplify the documents within a topical pattern. We show examples of computationally aided discovery and reading in three MOOC settings: mapping students’ self-reported motivations, identifying themes in discussion forums, and uncovering patterns of feedback in course evaluations. 

scholarly journals A survey on the pedagogical features of language massive open online courses

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billy Tak-ming Wong
Keyword(s):  
Language Learning ◽  
Language Proficiency ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Learning Activities ◽  
Computer Assisted ◽  
Learning Technology ◽  
Discussion Forums ◽  
Massive Open Online ◽  
Content Type

PurposeThis paper examines the pedagogical features of massive open online courses (MOOCs) for language learning–known as language MOOCs. The mainstream pedagogy of MOOCs typically involves the provision of short videos and reading materials for self-study; discussion forums, mostly for peer-to-peer interaction on course content; and machine-graded quizzes for self-assessment. For language learning, which has been conventionally understood as skill development, the pedagogical features of relevant MOOCs have yet to be comprehensively surveyed.Design/methodology/approachThis study surveyed a total of 123 language MOOCs from the major MOOC platforms. The pedagogical features shown in these courses were identified and categorised according to the types of course materials and learning activities as well as the participation of learners and instructors.FindingsEnglish was the most common language taught in the courses. Over 80% of the courses took not more than six hours to complete. Most of these courses followed the typical approach of xMOOC delivery, with video watching, reading and auto-graded assessment being the most common learning activities. Less than half of the courses included discussion as part of learning, and instructors were involved in less than 30% of the discussion.Originality/valueThe findings show that, despite the technological advances in course delivery, current language MOOCs do not differ substantially from conventional distance language learning. Yet, the utilisation of computer-assisted language learning technology and the massive student base of MOOCs for creating a virtual social community are opportunities for developing learners' language proficiency on this learning environment.

scholarly journals Technological Innovations in Large-Scale Teaching: Five Roots of Massive Open Online Courses

2017 ◽  
Vol 56 (5) ◽  
pp. 623-644 ◽  
Author(s):  
Sandra Sanchez-Gordon ◽  
Sergio Luján-Mora
Keyword(s):  
Large Scale ◽  
Online Courses ◽  
Careful Consideration ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Massive Open Online Courses ◽  
Technological Innovations ◽  
Computer Assisted ◽  
Successful Implementation ◽  
Massive Open Online

There are millions of people worldwide—of all ages, conditions, backgrounds, and motivations—with significant learning needs. Unfortunately, traditional education is not efficient enough to meet these needs. That is, the available educational resources are not fully exploited to help cover the demand. There is an increasing need for large-scale access to cost-effective and high-quality education. The use of technological innovations for large-scale teaching might be part of the solution. In this context, the goals of this study were to identify technological innovations that can be considered historical milestones in large-scale teaching, to systematize experts’ opinions about the topic, and to propose strategies for the successful implementation of massive open online courses (MOOCs). The researchers identified and analyzed a documentary corpus and found that, in the use of technologies for large-scale teaching, there has been a parallel evolution that have led to the emergence of MOOCs and includes five roots: distance education and online learning, testing or teaching machines and computer-assisted instruction, learning management systems, open education and open educational resources, and online massive teaching. The researchers propose three strategies for the successful implementation of MOOCs: careful consideration of the c/x MOOC pedagogical spectrum characteristics, selection of an appropriate MOOC model, and management of implementation challenges.

Unfolding Sentimental and Behavioral Tendencies of Learners' Concerned Topics From Course Reviews in a MOOC

2018 ◽  
Vol 57 (3) ◽  
pp. 670-696 ◽  
Author(s):  
Sannyuya Liu ◽  
Xian Peng ◽  
Hercy N. H. Cheng ◽  
Zhi Liu ◽  
Jianwen Sun ◽  
...  
Keyword(s):  
Large Scale ◽  
Topic Model ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Video Production ◽  
Feedback Channel ◽  
Massive Open Online ◽  
Behavioral Tendencies ◽  
Negative Sentiment ◽  
Interactive Feedback

Course reviews, which is designed as an interactive feedback channel in Massive Open Online Courses, has promoted the generation of large-scale text comments. These data, which contain not only learners' concerns, opinions and feelings toward courses, instructors, and platforms but also learners' interactions (e.g., post, reply), are generally subjective and extremely valuable for online instruction. The purpose of this study is to automatically reveal these potential information from 50 online courses by an improved unified topic model Behavior-Sentiment Topic Mixture, which is validated and effective for detecting frequent topics learners discuss most, topics-oriented sentimental tendency as well as how learners interact with these topics. The results show that learners focus more on the topics about course-related content with positive sentiment, as well as the topics about course logistics and video production with negative sentiment. Moreover, the distributions of behaviors associated with these topics have some differences.

423 Massive Open Online Courses in Anatomy Education: A Systematic Review

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
J Cheong ◽  
I Keenan
Keyword(s):  
Systematic Review ◽  
Medical Education ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Learning Approaches ◽  
Discussion Forums ◽  
Massive Open Online ◽  
Pubmed Database ◽  
Literature Search Strategy ◽  
Learning Development

Abstract Introduction Massive open online courses (MOOCs) have become increasingly popular for remotely delivering education to global audiences. MOOCs can provide an online platform for participants coming from a variety of professional backgrounds and international locations. Our systematic review investigated current literature surrounding MOOCs, and identified the value of such courses with respect to anatomical and medical education. Method Our literature search strategy involved exploring the PubMed database for the terms ’MOOC + Anatomy’ and ‘MOOC + Medical Education’ identified 290 relevant articles. Following implementation of the PRISMA criteria, 24 articles out of 290 were selected for inclusion in our systematic review. Results Participants completing MOOCs in anatomical and medical education generally showed increased knowledge and skills. We found that MOOC discussion forums encourage participants’ social learning development. MOOCs have shown to address participants’ needs and expectations while supplementing traditional learning approaches. However, the majority of experimental research studies did not include pre-post testing or appropriate controls. Furthermore, high levels of participant attrition, inability to address all learning approaches were identified as limitations with respect to MOOCs. Conclusions Although MOOCs have shown success in providing an excellent platform for learning, it has yet to show definitive improvements when compared to traditional teaching methods.

Mining Individual Learning Topics in Course Reviews Based on Author Topic Model

2017 ◽  
Vol 15 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Sanya Liu ◽  
Cheng Ni ◽  
Zhi Liu ◽  
Xian Peng ◽  
Hercy N.H. Cheng
Keyword(s):  
Topic Model ◽  
Online Courses ◽  
Rapid Development ◽  
Educational Data Mining ◽  
Online Reviews ◽  
Massive Open Online Courses ◽  
Massive Open Online ◽  
Analysis Technique ◽  
Learning Analysis

Nowadays, Massive Open Online Courses (MOOC) has obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOC, a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for the each learner. According to the experimental results, we will analyze and focuses of interests of learners, which facilitates further personalized course recommendation and improve the quality of online courses.

Understanding learner participation at scale: How and why

2020 ◽  
Vol 17 (3) ◽  
pp. 236-252
Author(s):  
Samaa Haniya ◽  
Luc Paquette
Keyword(s):  
Teaching And Learning ◽  
Large Scale ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Online Course ◽  
Massive Open Online ◽  
Learner Participation ◽  
Massive Open Online Course ◽  
Motivation For Learning ◽  
E Learning

Understanding learner participation is essential to any learning environment to enhance teaching and learning, especially in large scale digital spaces, such as massive open online courses. However, there is a lack of research to fully capture the dynamic nature of massive open online courses and the different ways learners participate in these emerging massive e-learning ecologies. To fill in the research gap, this paper attempted to investigate the relationship between how learners choose to participate in a massive open online course, their initial motivation for learning, and the barriers they faced throughout the course. This was achieved through a combination of data-driven clustering approaches—to identify patterns of learner participation—and qualitative analysis of survey data—to better understand the learners’ motivation and the barriers they faced during the course. Through this study we show how, within the context of a Coursera massive open online course offered by the University of Illinois, learners with varied patterns of participation (Advanced, Balanced, Early, Limited, and Delayed Participation) reported similar motivations and barriers, but described differences in how their participation was impacted by those factors. These findings are significant to gain insights about learners’ needs which in turn serve as the basis to innovate more adaptive and personalized learning experiences and thus advance learning in these large scale environments.

Knowledge Discovery of Student Sentiments in MOOCs and Their Impact on Course Performance

2014 ◽  
Author(s):  
Conrad S. Tucker ◽  
Bryan Dickens ◽  
Anna Divinsky
Keyword(s):  
Student Performance ◽  
Language Processing ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Massive Open Online ◽  
Course Performance ◽  
Student Interactions ◽  
Computing Device ◽  
Digital World ◽  
Textual Data

The objective of this research is to mine textual data (e.g., online discussion forums) generated by students enrolled in Massive Open Online Courses (MOOCs) in order to quantify students’ sentiment, in relation to their course performance. Massive Open Online Courses (MOOCs) are free to anyone with a computing device and a means of connecting to the internet and serve as a new paradigm for distance based education. While student interactions in traditional based brick and mortar classes are readily observable by students and instructors, quantifying the sentiments expressed by students in MOOCs remains challenging. This is in part due to the quantity of textual data being generated by students enrolled in MOOCs, in addition to a lack of quantitative methodologies that discover latent, previously unknown knowledge pertaining to student interactions and sentiments in the digital world. The authors of this work introduce a data mining driven methodology that employs natural language processing techniques and text mining algorithms to quantify students’ sentiments, based on their textual data provided during course assignment discussions. The researchers of this work aim to help educators understand the factors that may impact student performance, team interactions and overall learning outcomes in digital environments such as MOOCs.

scholarly journals E-learning Text Sentiment Classification Using Hierarchical Attention Network (HAN)

2021 ◽  
Vol 16 (13) ◽  
pp. 157
Author(s):  
Abdessamad Chanaa ◽  
Nour-eddine El Faddouli
Keyword(s):  
Online Courses ◽  
Research Field ◽  
Massive Open Online Courses ◽  
Analysis Tool ◽  
Classification Models ◽  
Discussion Forums ◽  
Hierarchical Network ◽  
Massive Open Online ◽  
Attention Network ◽  
E Learning

Massive Open Online Courses (MOOCs) have recently become a very motivating research field in education. Analyzing MOOCs discussion forums presents important issues since it can create challenges for understanding and appropriately identifying student sentiment behaviours. Using the high effectiveness of deep learning, this study aims to classify forum posts based on their sentiment polarity using two experiments. The first use the three known sentiment labels (positive/negative/neutral) and the second one employs sevens labels. The classification method implemented the Hierarchical Attention Network (HAN) algorithm; it combines the attention mechanism with a hierarchical network that simulates the same hierarchical structure of the document. The analysis of 29604 discussion posts from Stanford University affirms the effectiveness of our model. HAN achieved a classification accuracy of 70.3%, which surpassed the other prediction results using usual text classification models. These results are promising and have implications on the future development of automated sentiment analysis tool on e-learning discussion forum.

scholarly journals Computer-Assisted Reading and Discovery for Student Generated Text in Massive Open Online Courses

2014 ◽  
Author(s):  
Justin Reich ◽  
Dustin H. Tingley ◽  
Jetson Leder-Luis ◽  
Margaret E. Roberts ◽  
Brandon Stewart
Keyword(s):  
Online Courses ◽  
Massive Open Online Courses ◽  
Computer Assisted ◽  
Massive Open Online ◽  
Assisted Reading

scholarly journals Types of Participant Behavior in a Massive Open Online Course

2017 ◽  
Vol 18 (6) ◽  
Author(s):  
Tali Kahan ◽  
Tal Soffer ◽  
Rafi Nachmias
Keyword(s):  
Online Courses ◽  
Massive Open Online Courses ◽  
Online Course ◽  
Discussion Forums ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Video Lectures ◽  
Different Types ◽  
Course Achievement ◽  
Participant Behavior

<p class="3">In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better evaluate the phenomenon and to support MOOCs developers and instructors in devising courses which are adapted for different learners' needs. Thus, the purpose of this study was to characterize the different types of participant behavior in a MOOC. Using a data mining methodology, 21,889 participants of a MOOC were classified into clusters, based on their activity in the main learning resources of the course: video lectures, discussion forums, and assessments. Thereafter, the participants in each cluster were characterized in regard to demographics, course participation, and course achievement characteristics. Seven types of participant behavior were identified: <em>Tasters</em> (64.8%), <em>Downloaders</em> (8.5%), <em>Disengagers</em> (11.5%), <em>Offline</em> <em>Engagers</em> (3.6%), <em>Online Engagers</em> (7.4%), <em>Moderately Social Engagers</em> (3.7%), and <em>Social Engagers</em> (0.6%). A significant number of 1,020 participants were found to be engaged in the course, but did not achieve a certificate. The types are discussed according to the established research questions. The results provide further evidence regarding the utilization of the flexibility, which is offered in MOOCs, by the participants according to their needs. Furthermore, this study supports the claim that MOOCs' impact should not be evaluated solely based on certification rates but rather based on learning behaviors.</p>

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