Using Expert Reviews to Enhance Learning Designs

2011 ◽  
pp. 243-262 ◽  
Author(s):  
Carmel McNaught ◽  
Paul Lam ◽  
Kin-Fai Cheng
Keyword(s):  
Hong Kong ◽  
Learning Strategies ◽  
Web Sites ◽  
Learning Design ◽  
Expert Review ◽  
Institutional Planning ◽  
Chinese University ◽  
E Learning ◽  
Evaluation Matrix ◽  
The Web

The chapter will describe an expert review process used at The Chinese University of Hong Kong. The mechanism used involves a carefully developed evaluation matrix which is used with individual teachers. This matrix records: (1) the Web functions and their use as e-learning strategies in the course Web site; (2) how completely these functions are utilized; and (3) the learning design implied by the way the functions selected are used by the course documentation and gauged from conversations with the teacher. A study of 20 course Web sites in the academic years 2005–06 and 2006–07 shows that the mechanism is practical, beneficial to individual teachers, and provides data of relevance to institutional planning for e-learning.

Examining Diffusion and Sustainability of E-Learning Strategies through Weblog Data

2010 ◽  
Vol 2 (3) ◽  
pp. 39-52 ◽  
Author(s):  
Paul Lam ◽  
Judy Lo ◽  
Antony Yeung ◽  
Carmel McNaught
Keyword(s):  
Online Learning ◽  
Hong Kong ◽  
Learning Strategies ◽  
Learning Management Systems ◽  
Learning Activities ◽  
Management Systems ◽  
Vertical Diffusion ◽  
Chinese University ◽  
Teachers And Students ◽  
E Learning

The study focuses on ‘horizontal’ and ‘vertical’ adoption of e-learning strategies at The Chinese University of Hong Kong as revealed through computer log records in the centrally supported learning management systems. Horizontal diffusion refers to whether e-learning has spread to influence the practice of more teachers and students. In vertical diffusion, the authors examined whether or not teachers tend to adopt more varied online learning activities in successive years. The overall findings are that, while adoption of simple strategies is increasing, there is little evidence of horizontal and vertical diffusion of more complex strategies. Indeed, the use of some of the more complex strategies, which may relate to greater potential learning benefits, decreased. Results have led to discussions about new focuses and strategies for our institutional eLearning Service.

E-learning and Language and Style in Hong Kong

2006 ◽  
Vol 15 (3) ◽  
pp. 277-290 ◽  
Author(s):  
Peter Crisp
Keyword(s):  
Hong Kong ◽  
Cultural Context ◽  
Quantitative Data ◽  
Web Based ◽  
Traditional Lecture ◽  
Chinese University ◽  
E Learning ◽  
Approach To Teaching ◽  
The Web ◽  
Qualitative Discussion

This article presents something of the experience of teaching and studying web-based Language and Style at the Chinese University of Hong Kong, concentrating mainly on the 2004–5 running of the course, which was taught in a blended traditional + web format. It concentrates on this experience and only briefly presents some degree of the socio-linguistic and cultural context to make the experience accessible for those not familiar with Hong Kong. Some quantitative data are given and are supplemented by qualitative discussion of student comments. A particularly important qualitative resource was the weekly journals kept by the students. The main conclusion to be drawn is that the students want to retain the major features of the traditional lecture and tutorial approach to teaching, but value having this supplemented with the interactive dimension of the web-based approach.

Examining Diffusion and Sustainability of E-Learning Strategies through Weblog Data

2012 ◽  
pp. 258-272
Author(s):  
Paul Lam ◽  
Judy Lo ◽  
Antony Yeung ◽  
Carmel McNaught
Keyword(s):  
Online Learning ◽  
Hong Kong ◽  
Learning Strategies ◽  
Learning Management Systems ◽  
Learning Activities ◽  
Management Systems ◽  
Vertical Diffusion ◽  
Chinese University ◽  
Teachers And Students ◽  
E Learning

The study focuses on ‘horizontal’ and ‘vertical’ adoption of e-learning strategies at The Chinese University of Hong Kong as revealed through computer log records in the centrally supported learning management systems. Horizontal diffusion refers to whether e-learning has spread to influence the practice of more teachers and students. In vertical diffusion, the authors examined whether or not teachers tend to adopt more varied online learning activities in successive years. The overall findings are that, while adoption of simple strategies is increasing, there is little evidence of horizontal and vertical diffusion of more complex strategies. Indeed, the use of some of the more complex strategies, which may relate to greater potential learning benefits, decreased. Results have led to discussions about new focuses and strategies for our institutional eLearning Service.

scholarly journals Machine Learning Based Text Document Classification for E-Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 194-201
Keyword(s):  
Machine Learning ◽  
Web Sites ◽  
Document Classification ◽  
Classification Model ◽  
Classification Algorithms ◽  
Testing Time ◽  
Machine Learning Classification ◽  
Text Document ◽  
E Learning ◽  
The Web

The number of e-learning websites as well as e-contents are increasing exponentially over the years and most of the time it become cumbersome for a learner to find e-content suitable for learning as the learner gets overwhelmed by the enormity of the content availability. The proposed work focus on evaluating the efficiencies of the different classification algorithm for the identification of the e-learning content based on difficulty levels. The data is collected from many e-learning web sites through web scraping. The web scraper downloads the web pages and parse to text file. The text files were made to run through many machine learning classification algorithms to find out the best classification model suitable for achieving the highest score with minimum training and testing time. This method helps to understand the performance of different text classification algorithms on e-learning contents and identifies the classifier with high accuracy for document classification.

Information and Visualization Imagery

2010 ◽  
pp. 89-117
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Decision Making ◽  
Learning Strategies ◽  
Data Streams ◽  
Memory Retention ◽  
Effective Learning ◽  
Interactive Tools ◽  
E Learning ◽  
Simulation Learning ◽  
Information Aesthetics ◽  
The Web

Information and visualization imagery conveys data for more effective learning and comprehension, memory retention; analytic tractability; decision-making and information aesthetics. These types of visualization imagery may be built both in simple and complex ways. Complex and live data streams may be collated and delivered via interactive tools delivered via the Web, with some bolstering simulation learning. This chapter addresses the types of visualizations used in e-learning, strategies for creating these, and the ways to avoid unintended and negative learning.

Five Factors for Planning

2011 ◽  
pp. 1-38
Author(s):  
Katy Campbell
Keyword(s):  
Development Process ◽  
Online Course ◽  
Learning Design ◽  
Planning Decisions ◽  
Five Factors ◽  
E Learning ◽  
Delivery Technology ◽  
The Web

This chapter is for a reader who is at the beginning of the writing or development process, or who wants to affirm planning decisions. In this chapter you will consider five planning aspects in e-Learning design: 1. Outcomes 2. Learners 3. Activities 4. Assessment 5. Resources An overview of each aspect will help you describe your own hopes and expectations for your online course. By the end of the chapter you should feel confident that the Web is a good delivery technology for your course.

Information and Visualization Imagery

2011 ◽  
pp. 752-778
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Decision Making ◽  
Learning Strategies ◽  
Data Streams ◽  
Memory Retention ◽  
Effective Learning ◽  
Interactive Tools ◽  
E Learning ◽  
Simulation Learning ◽  
Information Aesthetics ◽  
The Web

Information and visualization imagery conveys data for more effective learning and comprehension, memory retention; analytic tractability; decision-making and information aesthetics. These types of visualization imagery may be built both in simple and complex ways. Complex and live data streams may be collated and delivered via interactive tools delivered via the Web, with some bolstering simulation learning. This chapter addresses the types of visualizations used in e-learning, strategies for creating these, and the ways to avoid unintended and negative learning.

scholarly journals Predicting Chinese University Students’ E-Learning Acceptance and Self-Regulation in Online English Courses: Evidence From Emergency Remote Teaching (ERT) During COVID-19

SAGE Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 215824402110613
Author(s):  
Sijing Zhou ◽  
Yu Zhou ◽  
Huiling Zhu
Keyword(s):  
Technology Acceptance ◽  
Learning Strategies ◽  
Structural Equation ◽  
Qualitative Data ◽  
Course Design ◽  
Learning Experience ◽  
Self Regulation ◽  
Equation Modeling ◽  
Chinese University ◽  
E Learning

A growing concern for online course learning is to what extent learners are concentrated and self-regulated when they are isolated from their classmates and instructors. To address this issue, this study collected both quantitative and qualitative data from a sample of 580 Chinese university learners from varied majors, who were taking online English courses in Emergency Remote Teaching (ERT) mode during COVID-19. This study identified specific psychological and contextual factors that impact learners’ e-learning acceptance and online self-regulation, based upon Technology Acceptance Model (TAM). Learners’ actual use of three sub-processes of self-regulated strategies, namely, goal setting, task strategies, and self-evaluation was also examined. Partial least squares (PLS)-structural equation modeling (SEM) technique was used to test hypotheses and proposed research model. The quantitative results indicate that media richness, as a contextual factor, and social presence and flow, as two typical psychological factors, are determining antecedents that impact Chinese learners’ e-learning acceptance. Meanwhile, quantitative findings show that learners’ behavioral intention to use e-learning is a main contributor of their use of all three sub-processes of self-regulated learning strategies. Furthermore, thematic analysis was conducted to study the qualitative data, revealing that learners held rather divided and mixed perceptions regarding online learning experience. These findings have important implications for effective online English course design and implementation.

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