Activity sequence modelling and dynamic clustering for personalized e-learning

Köck, Mirjam; Paramythis, Alexandros

doi:10.1007/s11257-010-9087-z

Cited by 61 publications

(22 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using existing personality and strategy evidence, we demonstrated one approach to personalization. Our work, therefore, builds on the previous research on the importance of considering different learning characteristics such as personality and learning strategies in online settings [1][2][3].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing Efficiency of Prompts Based on Learner Characteristics

et al. 2017

View full text Add to dashboard Cite

Abstract:Personalized prompting research has shown the significant learning benefit of prompting. The current paper outlines and examines a personalized prompting approach aimed at eliminating performance differences on the basis of a number of learner characteristics (capturing learning strategies and traits). The learner characteristics of interest were the need for cognition, work effort, computer self-efficacy, the use of surface learning, and the learner's confidence in their learning. The approach was tested in two e-modules, using similar assessment forms (experimental n = 413; control group n = 243). Several prompts which corresponded to the learner characteristics were implemented, including an explanation prompt, a motivation prompt, a strategy prompt, and an assessment prompt. All learning characteristics were significant correlates of at least one of the outcome measures (test performance, errors, and omissions). However, only the assessment prompt increased test performance. On this basis, and drawing upon the testing effect, this prompt may be a particularly promising option to increase performance in e-learning and similar personalized systems.

show abstract

Section: Discussionmentioning

confidence: 99%

“…To date, many user and learner models personalize the learning experience by implementing tutoring support based on perceived or self-reported learner characteristics [2,3]. Prompts can help learners to engage more with the material but also reflect on their learning progress [4].…”

Section: Introductionmentioning

confidence: 99%

Assessing Efficiency of Prompts Based on Learner Characteristics

et al. 2017

View full text Add to dashboard Cite

show abstract

“…There are different types of relationship in mining techniques such as association rule mining (any relationships between variables), sequential pattern mining (temporal associations between variables), correlation mining (linear correlations between variables), and causal data mining (causal relationships between variables). In EDM, relationship mining is used to identify relationships between the students' online activities and the final marks [23] and to model learners' problem solving activity sequences [24].…”

Section: The Used Methodsmentioning

confidence: 99%

Which Contribution Does EDM Provide to Computer-Based Learning Environments?

Bousbia

Belamri

2013

Educational Data Mining

View full text Add to dashboard Cite

Educational Data Mining is a new growing research area that can be defined as the application of data mining techniques on raw data from educational systems in order to respond to the educational questions and problems, and also to discover the information hidden after this data. Over the last few years, the popularity of this field enhanced a large number of research studies that is difficult to surround and to identify the contribution of data mining techniques in educational systems. In fact, exploit and understand the raw data collected from educational systems can be ''a gold mine'' to help the designers and the users of these systems improving their performance and extracting useful information on the behaviors of students in the learning process. The use of data mining techniques in e-learning systems could be very interesting to resolve learning problems. Researchers' ambition is to respond to questions like: What can predict learners' success? Which scenario sequence is more efficient for a specific student? What are the student actions that indicate the learning progress? What are the characteristics of a learning environment allowing a better learning? etc. The current feedback allows detecting the usefulness of applying EDM on visualizing and describing the learning raw data. The predictions take also an interest, particularly the prediction of performance and learners' behaviors. The aim of this chapter is to establish a bibliographic review of the various studies made in the field of educational data mining (EDM) to identify the different aspects studied: the analyzed data, the objectives of these studies, the used techniques and the contribution of the application of these techniques in the field of computer based learning. The goal is not only to list the existing work but also to facilitate the use and the understanding of data mining techniques to help the educational field specialists to give their

show abstract

“…For that purpose, they defined five student's timedependent patterns of actions based on time traces of actions within the ITS. More recently, (Köck and Paramythis, 2011) adopt a clustering approach for detecting sequences of learner's actions in the Andes ITS. These studies only occur in high-constraint environment like ITS.…”

Section: Identifying Engagement In Digital Gamingmentioning

confidence: 99%

Identifying Learner’s Engagement in Learning Games - A Qualitative Approach based on Learner’s Traces of Interaction

Bouvier

Lavoué

Sehaba

et al. 2013

Proceedings of the 5th International Conference on Computer Supported Education

View full text Add to dashboard Cite

Abstract:This paper proposes a qualitative approach for identifying learners' engagement from their traces of interactions performed in the learning game. Learners' engagement is an effective indicator of their motivation, acceptance and attachment to the learning activity. Engagement also informs about the relevance of the content and the effectiveness of the proposed interactive learning game. Designers, practitioners and teachers need information about engagement for analysing, designing and validating the learning game and also for modifying and adapting learning games in order to maintain their effectiveness. Currently, most of the approaches provide quantitative information about learner's engaged-behaviours. Thus, our objective is to extract qualitative information from learners-generated data. In this paper, we propose an approach in three stages that combines theoretical works on engagement and engaged-behaviours, Activity Theory and Trace Theory. By relying on traces of interactions, this approach enables to identify engaged-behaviours in low-constraint interactive games, directly, continuously, under ecological conditions and over a long time period. Then we present the results of a user study that demonstrate the feasibility and the validity of our approach. This study has been conducted on twelve traces composed of several thousands of learner-generated data.

show abstract

Activity sequence modelling and dynamic clustering for personalized e-learning

Cited by 61 publications

References 58 publications

Assessing Efficiency of Prompts Based on Learner Characteristics

Assessing Efficiency of Prompts Based on Learner Characteristics

Which Contribution Does EDM Provide to Computer-Based Learning Environments?

Identifying Learner’s Engagement in Learning Games - A Qualitative Approach based on Learner’s Traces of Interaction

Contact Info

Product

Resources

About