Incorporating proactivity to context-aware recommender systems for e-learning

Recommender systems in e-learning have proved to be powerful tools to find suitable educational material during the learning experience. But traditional user request-response patterns are still being used to generate these recommendations. By including contextual information derived from the use of ubiquitous learning environments, the possibility of incorporating proactivity to the recommendation process has arisen. In this paper we describe methods to push proactive recommendations to e-learning systems users when the situation is appropriate without being needed their explicit request. As a result, interesting learning objects can be recommended attending to the user?s needs in every situation. The impact of this proactive recommendations generated have been evaluated among teachers and scientists in a real e-learning social network called Virtual Science Hub related to the GLOBAL excursion European project. Outcomes indicate that the methods proposed are valid to generate such kind of recommendations in e-learning scenarios. The results also show that the users' perceived appropriateness of having proactive recommendations is high

An Improved Apriori Algorithm for Association Rule Mining in Employability Analysis

The wide application of emerging advanced technologies causes significant changes in the development trend of the employment market. The lack of flexible and easy-to-implement analysis methods challenges general maritime education practitioners to understand the developing trends. This research proposed the improved Apriori algorithm to explore employment preference by identifying the association rule of the employability indicators and the employment status. The candidate generation methods are optimised based on the questionnaire design to generate fewer candidates. The minimum support value is automatically generated to reduce the reliance on analysis expertise and improve accuracy. To validate the algorithm, a questionnaire for the maritime graduate is used to collect employment data to test the efficiency and capability of the improved algorithm. The computation time for different data set sizes shows that the improvement could improve the algorithm\u27s effectiveness. The algorithm also successfully identifies significant employment preference that certain employment types emphasise specific employability skills, such as responsibility and core professional skills. The results suggest that the improved A algorithm could reduce the computing burden and identify the employment preference from questionnaire data. This research provides easy-to-use and flexible analysis tools, which could reduce the computing expertise required for education practitioners

Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study

Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below

MoodleMiner: Data Mining Analysis Tool for Moodle Learning Management System

The purpose of this study is to develop a tool through which non-experts can carry out basic data mining analyses on logs they obtained via Moodle Learning Management System. The study also includes the findings obtained by applying the developed tool on a data set from a real course. The developed tool automatically extracts the features regarding student interactions with the learning system by using their click-stream data, and analyzes this data by using the data mining libraries available in the R programming language. The tool has enabled the users who do not have any expertise in data mining or programming to automatically carry out data mining analyses. The information generated by the tool will help researchers and educators alike in grouping students by their interaction levels, determining at-risk students, monitoring students' interaction levels, and identifying important features that impact students’ academic performances. The data processed by the tool can also be exported to be used in various other analyses. In the future versions of the tool, it is planned to add different analyzes such as association rule mining, sequential pattern mining etc

Generating Knowledge in Maintenance from Experience Feedback

Knowledge is nowadays considered as a significant source of performance improvement, but may be difficult to identify, structure, analyse and reuse properly. A possible source of knowledge is in the data and information stored in various modules of industrial information systems, like CMMS (Computerized Maintenance Management Systems) for maintenance. In that context, the main objective of this paper is to propose a framework allowing to manage and generate knowledge from information on past experiences, for improving the decisions related to the maintenance activity. In that purpose, we suggest an original Experience Feedback process dedicated to maintenance, allowing to capitalize on past interventions by i) formalizing the domain knowledge and experiences using a visual knowledge representation formalism with logical foundation (Conceptual Graphs); ii) extracting new knowledge thanks to association rules mining algorithms, using an innovative interactive approach; iii) interpreting and evaluating this new knowledge thanks to the reasoning operations of Conceptual Graphs. The suggested method is illustrated on a case study based on real data dealing with the maintenance of overhead cranes

Aplicación de métodos de diseño centrado en el usuario y minería de datos para definir recomendaciones que promuevan el uso del foro en una experiencia virtual de aprendizaje

The use of recommendation systems in learning virtual environments is increasingly becoming a feasible approach for providing the adaptive support required to attend students’ learning needs. With the interaction data obtained from these virtual environments it is possible to find indicators where data mining and machine learning techniques can be applied to identify relevant information that allows for the definition of recommendations. In this research we have applied unsupervised learning techniques to identify common interaction patterns with available forums in a course on the OpenACS/dotLRN platform. This will allow recommendations to be defined that help improve the students’ learning experience.La adopción de sistemas recomendadores en ambientes virtuales de aprendizaje se está convirtiendo en una alternativa; para lograr la adaptación automática requerida, para atender las necesidades de aprendizaje de los estudiantes. Con los datos de interacción, que proveen estos ambientes es posible encontrar indicadores que con la aplicación de técnicas de minería de datos y aprendizaje automático se pueda identificar información relevante, para la definición de recomendaciones. En esta investigación, hemos aplicado técnicas de aprendizaje no supervisado, para la identificación de patrones comunes de interacción con los foros disponibles en un curso de la plataforma OpenACS/dotLRN. Esto facilitará la definición de recomendaciones que ayuden a mejorar la experiencia de aprendizaje de los estudiantes

System Design and Architecture of an Online, Adaptive, and Personalized Learning Platform

The authors propose that personalized learning can be brought to traditional and nontraditional learners through a new type of asynchronous learning platform called Guided Learning Pathways (GLP). The GLP platform allows learners to intelligently traverse a vast field of learning resources, emphasizing content only of direct relevance to the learner and presenting it in a way that matches the learner’s pedagogical preference and contextual interests. GLP allows learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner’s interests and motivations. Learning communities would support learners moving through similar topics. This report describes the software system design and architecture required to support Guided Learning Pathways. The authors provide detailed information on eight software applications within GLP, including specific learning benefits and features of each. These applications include content maps, learning nuggets, and nugget recommendation algorithms. A learner scenario helps readers visualize the functionality of the platform. To describe the platform’s software architecture, the authors provide conceptual data models, process flow models, and service group definitions. This report also provides a discussion on the potential social impact of GLP in two areas: higher education institutions and the broader economy

System design and architecture of an online, adaptive, and personalized learning platform

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 76-82).The author proposes that personalized learning can be brought to traditional and nontraditional learners through a new type of asynchronous learning platform called Guided Learning Pathways (GLP). The GLP platform allows learners to intelligently traverse a vast field of learning resources, emphasizing content only of direct relevance to the learner and presenting it in a way that matches the learner's pedagogical preference and contextual interests. GLP allows learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner's interests and motivations. Learning communities would support learners moving through similar topics. This thesis describes the software system design and architecture required to support Guided Learning Pathways. The author provides detailed information on eight software applications within GLP, including specific learning benefits and features of each. These applications include content maps, learning nuggets, and nugget recommendation algorithms. A learner scenario helps readers visualize the functionality of the platform. To describe the platform's software architecture, the author provides conceptual data models, process flow models, and service group definitions. This thesis also provides a discussion on the potential social impact of GLP in two areas: higher education institutions and the broader economy.by Cole J. Shaw.S.M.in Technology and Polic