Involving Users to Improve the Collaborative Logical Framework

In order to support collaboration in web-based learning, there is a need for an intelligent support that facilitates its management during the design, development, and analysis of the collaborative learning experience and supports both students and instructors. At aDeNu research group we have proposed the Collaborative Logical Framework (CLF) to create effective scenarios that support learning through interaction, exploration, discussion, and collaborative knowledge construction. This approach draws on artificial intelligence techniques to support and foster an effective involvement of students to collaborate. At the same time, the instructors’ workload is reduced as some of their tasks—especially those related to the monitoring of the students behavior—are automated. After introducing the CLF approach, in this paper, we present two formative evaluations with users carried out to improve the design of this collaborative tool and thus enrich the personalized support provided. In the first one, we analyze, following the layered evaluation approach, the results of an observational study with 56 participants. In the second one, we tested the infrastructure to gather emotional data when carrying out another observational study with 17 participants

A new agglomerative hierarchical clustering to model student activity in online learning

In this paper, a new technique of agglomerative hierarchical clustering (AHC), which is known as SLG (single linkage dissimilarity increment distribution, global cumulative score standard), can work well in analyzing students' activity in online learning as evidenced by obtaining the highest score in testing the validity index of cophenetic correlation coefficient (CPCC) ie 0.9237, 0.9015, 0.9967, 0.8853, 0.9875 of the five datasets compared with conventional agglomerative hierarchical clustering methods

An intelligent framework for monitoring student performance using fuzzy rule-based linguistic summarisation

Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning in order to better engage students with the subject and improve their understanding of the material being taught. We describe the use of a fuzzy Linguistic Summarisation (LS) technique for extracting linguistically interpretable scaled fuzzy weighted rules from student data describing prominent relationships between activity / engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem based learning tasks. The system can be used to more effectively evaluate new teaching approaches and methodologies, identify weaknesses and provide more personalised feedback on learner's progress. We present a case study and initial experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a Group Performance Model (GPM) to deploy Activity Led Learning (ALL) in a Master-level module. Results show that the fuzzy weighted rules can identify useful relationships between student engagement and performance providing a mechanism allowing educators to transparently evaluate teaching and factors effecting student performance, which can be incorporated as part of an automated intelligent analysis and feedback system

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

Personality and Education Mining based Job Advisory System

Every job demands an employee with some specific qualities in addition to the basic educational qualification. For example, an introvert person cannot be a good leader despite of a very good academic qualification. Thinking and logical ability is required for a person to be a successful software engineer. So, the aim of this paper is to present a novel approach for advising an ideal job to the job seeker while considering his personality trait and educational qualification both. Very well-known theories of personality like MBTI indicator and OCEAN theory, are used for personality mining. For education mining, score based system is used. The score based system captures the information from attributes like most scoring subject, dream job etc. After personality mining, the resultant values are coalesced with the information extracted from education mining. And finally, the most suited jobs, in terms of personality and educational qualification are recommended to the job seekers. The experiment is conducted on the students who have earned an engineering degree in the field of computer science, information technology and electronics. Nevertheless, the same architecture can easily be extended to other educational degrees also. To the best of the author’s knowledge, this is a first e-job advisory system that recommends the job best suited as per one’s personality using MBTI and OCEAN theory both