Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners

Data Mining ◽  
2013 ◽  
pp. 1916-1935
Author(s):  
Mingming Zhou ◽  
Yabo Xu
Keyword(s):  
Learning Environment ◽  
Cognitive Skills ◽  
Cognitive Activity ◽  
Cognitive Strategies ◽  
Monitoring And Control ◽  
Online Learners ◽  
Data Mining Algorithms ◽  
E Learning ◽  
And Performance ◽  
Mining Algorithms

A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning environment designs, however, meta-cognitive strategies have generally been neglected, and therefore, satisfactory uses of these strategies have rarely been realized. Most learners are not even aware of what they have been studying. If the learning system could automatically guide and intelligently recommend learning activities or strategies to facilitate student monitoring and control of their learning, it would favor and improve their learning process and performance. Unfortunately, nearly no e-learning systems to date have attempted to do so. In this chapter, we first described the need for enhancing meta-cognitive skills in e-learning environment, followed by an outline of major challenges for meta-cognitive activity recommendations. We then proposed to adopt data mining algorithms (i.e., content-based and sequence-based recommendation techniques) to meet the identified issues with a toy example.

Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners

2012 ◽  
pp. 282-301 ◽  
Author(s):  
Mingming Zhou ◽  
Yabo Xu
Keyword(s):  
Learning Environment ◽  
Cognitive Skills ◽  
Cognitive Activity ◽  
Cognitive Strategies ◽  
Monitoring And Control ◽  
Online Learners ◽  
Data Mining Algorithms ◽  
E Learning ◽  
And Performance ◽  
Mining Algorithms

A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning environment designs, however, meta-cognitive strategies have generally been neglected, and therefore, satisfactory uses of these strategies have rarely been realized. Most learners are not even aware of what they have been studying. If the learning system could automatically guide and intelligently recommend learning activities or strategies to facilitate student monitoring and control of their learning, it would favor and improve their learning process and performance. Unfortunately, nearly no e-learning systems to date have attempted to do so. In this chapter, we first described the need for enhancing meta-cognitive skills in e-learning environment, followed by an outline of major challenges for meta-cognitive activity recommendations. We then proposed to adopt data mining algorithms (i.e., content-based and sequence-based recommendation techniques) to meet the identified issues with a toy example.

scholarly journals The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1295 ◽  
Author(s):  
Mohiuddin Ahmed ◽  
Raihan Seraj ◽  
Syed Mohammed Shamsul Islam
Keyword(s):  
Experimental Analysis ◽  
Clustering Algorithm ◽  
Fundamental Problem ◽  
Clustering Algorithms ◽  
Data Types ◽  
Data Mining Algorithms ◽  
Recent Developments ◽  
Comprehensive Survey ◽  
And Performance ◽  
Mining Algorithms

The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.

Temporal experiences of e-learning by distance learners

2014 ◽  
Vol 56 (2/3) ◽  
pp. 179-189 ◽  
Author(s):  
Beverly Leeds
Keyword(s):  
Learning Environment ◽  
Culture Shock ◽  
Time Preferences ◽  
Discussion Boards ◽  
Online Learners ◽  
Content Type ◽  
Telephone Interviews ◽  
Technology Changes ◽  
Temporal Flexibility ◽  
E Learning

Purpose – The purpose of this paper is to explore experiences of e-learners in relation to their time preferences, and to examine how technology changes temporal culture in e-learning. Design/methodology/approach – A pilot study using multiple methods of data collection to provide a rich picture of the experience of e-learners in relation to temporal flexibility. Taking a narrative approach it analysed data from a survey, time tracking data as well as data from online discussion boards, learners’ assignments and short telephone interviews. Findings – The study found e-learners may experience temporal culture shock when starting to study online for the first time. It highlights the need to explore an individual's time preference and to consider the temporal culture of the e-learning programme. Research limitations/implications – It highlights that the issue of time is still missing from educational studies and suggests that temporal aspects are included in e-learning research studies. This is a small exploratory study but it indicates the need for further research to explore the potential for temporal culture shock and influence of e-learner time preferences. Practical implications – Designing an e-learning environment for learners needs to consider learner time preferences to ensure that temporal flexibility is a realised benefit, not a barrier to learning. The temporal culture of the online learning environment also needs to be made explicit so that learner expectations can be managed. Originality/value – It contributes to the literature on e-learning in the higher education context by providing a temporal lens to explore learner experiences. It is of value to educationalists and learning technologists designing, delivering or evaluating e-learning programmes. Online learners may also benefit from its findings.

scholarly journals Students as a subject of the e-learning environment

Eduweb ◽  
2021 ◽  
Vol 15 (3) ◽  
pp. 102-112
Author(s):  
Nadija Figol ◽  
Vasyl Teremko ◽  
Iryna Pobidash ◽  
Aelita Lytvyn ◽  
Tеtiana Skorokhod ◽  
...  
Keyword(s):  
Learning Environment ◽  
Personal Responsibility ◽  
Cognitive Activity ◽  
Positive Outcome ◽  
Personal Attributes ◽  
Electronic Learning ◽  
Student Community ◽  
Self Organized ◽  
E Learning ◽  
Electronic Learning Environment

The article is devoted to the subjective role of the student community in the e-learning environment. The basic personal attributes of a modern student required for successful learning in the electronic learning environment were defined, including proactive attitude, motivation, creativity, independence, persistence. The tasks that should be overcome by students to achieve the goal of successful learning were outlined as follows: to take personal responsibility for the process and the final result of learning, to increase their motivation and interest in a positive outcome, to be able to develop communication with other students and the lecturer, to associate the acquired theoretical knowledge with their experience and practical skills. Distant learners were outlined to face a variety of challenges from technical issues and lack of guidance materials to communication difficulties, which are just as important. The conclusion to be drawn is that the functions and competencies of both students and teachers are changing in the e-learning environment. Communication moves to a different level and starts to be from a peer to peer perspective. The e-learning environment requires students to be more self-organized, goal-oriented, self-conscious in the process of education, to realize the purpose of their cognitive activity, to be inquisitive and passionate.

scholarly journals The Impact of Using Blackboard on Student’s Grades

2021 ◽  
Vol 15 (23) ◽  
pp. 148-163
Author(s):  
Dalia Alshmasi ◽  
Jawaher Alharbi ◽  
Nada Alhothly
Keyword(s):  
Male Students ◽  
Learning Tools ◽  
Core System ◽  
Face To Face ◽  
Data Mining Algorithms ◽  
E Learning ◽  
Face To Face Learning ◽  
Student Information ◽  
Mining Algorithms ◽  
The Impact

The Learning Management System (LMS) is a core system that contributes to support e-learning, managing content, provide learning tools, and facilitate communication between students and teachers. This project aims to measure the impact of Blackboard-based e-learning use. We measure the effect of blackboard use on male students by comparing male and female students ’degrees of online learning (online class preparation) and traditional (face-to-face) learning at Qassim University, using statistical methods and data mining algorithms. We also develop A supportive tool that helps teachers manage some important tasks such as (student attendance, interaction, grades ... etc.). This tool will take student information and push automatic notifications to all concerned people via SMS.

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