Supporting Undergraduate Research: Recommending Personalized Research Projects to Undergraduates

Undergraduates’ participation in faculty-mentored research is becoming an important issue in tertiary education in recent years, and it benefits both undergraduates and faculty members. In reality, many faculty members have research projects that need help from undergraduates, but undergraduates can hardly find the information, which creates information asymmetry problem. Besides, undergraduates lack the experience of doing academic research, the research interest information is incomplete, so they have difficulties in choosing suitable research projects. Thus recommender systems are necessary to facilitate undergraduates’ participation in research projects. Traditional recommendation approaches require relative complete information for decision making, and they can hardly meet the requirements as undergraduates’ research information is incomplete. In this study, we propose a two-stage model that integrates content-based method with collaborative method by leveraging research social networks, where undergraduates are encouraged to connect with faculty members and participate in social network activities, through which research information is collected. The proposed two-stage model alleviates the problems of information asymmetry and incomplete information. The recommender system has been developed in ScholarMate (www.scholarmate.com), and it allows undergraduates to choose suggested research projects

Panorama of Recommender Systems to Support Learning

This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424). Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders (FWO). Olga C. Santos would like to acknowledge that her contributions to this work have been carried out within the project Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts (MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported with funding CIP-PSP Open Discovery Space (297229

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles

Explosive growth of e-learning in the recent years has faced difficulty of locating appropriate learning resources to match the students learning styles. Recommender system is a promising technology in e-learning environments to present personalised offers and convey appropriate learning objects that match student inclinations. This paper, proposes a novel and effective recommender algorithm that recommends personalised learning objects based on the student learning styles. Various similarity metrics are considered in an experimental study to investigate the best similarity metrics to use in a recommender system for learning objects. The approach is based on the Felder and Silverman learning style model which is used to represent both the student learning styles and the learning object profiles. It was found that the K-means clustering algorithm, the cosine similarity metrics and the Pearson correlation coefficient are effective tools for implementing learning object recommender systems. The accuracy of the recommendations are measured using traditional evaluation metrics, namely the Mean Absolute Error and the Root Mean Squared Error

A Novel Adaptation Model for E-Learning Recommender Systems Based on Student’s Learning Style

In recent years, a substantial increase has been witnessed in the use of online learning resources by learn- ers. However, owing to an information overload, many find it difficult to retrieve appropriate learning resources for meeting learning requirements. Most of the existing systems for e-learning make use of a “one-size-fits-all” approach, thus providing all learners with the same content. Whilst recommender systems have scored notable success in the e-commerce domain, they still suffer from drawbacks in terms of making the right recommendations for learning resources. This can be attributed to the differences among learners’ preferences such as varying learning styles, knowledge levels and sequential learning patterns. Hence, to identify the needs of an individual student, e-learning systems that can build profiles of student preferences are required. In addition, changing students’ preferences and multidimensional attributes of the course content are not fully considered simultaneously. It is by failing to review these issues that existing recommendation algorithms often give inaccurate recommendations. This thesis focuses on student learning styles, with the aim of dynamically tailoring the learning process and course content to meet individual needs. The proposed Ubiquitous LEARNing (ULEARN) system is an adaptive e-learning recommender system geared towards providing a personalised learning environ- ment, which ensures that course learning objects are in line with the learner’s adaptive profile. This thesis delivers four main contributions: First, an innovative algorithm which dynamically reduces the number of questions in the Felder-Silverman Learning Styles (FSLSM) questionnaire for the purpose of initialising student profiles has been proposed. The second contribution comprises examining the accuracy of various similarity metrics so as to select the most suitable similarity measurements for learning objects recommendation algorithm. The third contribution includes an Enhanced Collaboration Filtering (ECF) algorithm and an Enhanced Content-Based Filtering (ECBF) algorithm, which solves the issues of cold-start and data sparsity in- herent to the traditional Collaborative Filtering (CF) and the traditional Content-based Filtering (CBF), respectively. Moreover, these two new algorithms have been combined to create a new Enhanced Hybrid Filtering (EHF) algorithm that recommends highly accurate personalised learning objects on the basis of the stu- dents’ learning styles. The fourth contribution is a new algorithm that tracks patterns of student learning behaviours and dynam- ically adapts the student learning style accordingly. The ULEARN recommendation system was implemented with Visual Studio in C++ and Windows Pre- sentation Foundation (WPF) for the development of the Graphical User Interface (GUI). The experimental results revealed that the proposed algorithms have achieved significant improvements in student’s profile adaptation and learning objects recommendation in contrast with strong benchmark models. Further find- ings from experiments indicated that ULEARN can provide relevant learning object recommendations based on students’ learning styles with the overall students’ satisfaction at almost 90%. Furthermore, the results showed that the proposed system is capable of mitigating the problems data sparsity and cold-start, thereby improving the accuracy and reliability of recommendation of the learning object. All in all, the ULEARN system is competent enough to support educational institutions in recommending personalised course content, improving students’ performance as well as promoting student engagement.Arab academy for science technology & maritime transpor

Pengembangan Sistem Pencarian OPAC Berbasis Social Shopping

OPAC searching system is developed with the main goal is to facilitate searching information as well as giving the relevant information to the searchers or browsers. Technology web 2.0 facilitate the internet users to interact widely, share idea/information or give comment to one content. The high product selling is influenced by one of the factors, that is, the recommendation from other people or community. Therefore, it can be the opportunity for the librarians to develop OPAC searching system with adopt the technology web 2.0. The users can directly customize and personalize the library services based on the experience of OPAC system. Feedback system with the feature relevance feedback on OPAC means that the users are given space to give input or other information on the searching results, so the document needed is the most relevant document to the users’ needs. Hybrid recommendations feature on OPAC, means the users are given space to get the seraching results based on the similar interest among the other users (content based filtering); and the searching results are also based on  the recomemendation with the greatest or the highest rating (collaborative filtering). The library needs to provide user profile, in order to be the basic of the hybrid recommendations feature development. Searching system with the hybrid recommendation feature and relevance feedback which are adopted from social shopping can be the guidance to develop OPAC searching system based on the technology web 2.0 in the future. Recommendation system which is built can increase the collection use, the source of evaluation to develop collections, information system as well as the library services.Keywords: OPAC, feedback, personalization, relevance feedback, web 2.0, social shopping, hybrid recommendations.                   

Improving Cloud System Reliability Using Autonomous Agent Technology

Cloud computing platforms provide efficient and flexible ways to offer services and computation facilities to users. Service providers acquire resources according to their requirements and deploy their services in cloud. Service consumers can access services over networks. In cloud computing, virtualization techniques allow cloud providers provide computation and storage resources according to users’ requirement. However, reliability in the cloud is an important factor to measure the performance of a virtualized cloud computing platform. Reliability in cloud computing includes the usability and availability. Usability is defined as cloud computing platform provides functional and easy-to-use computation resources to users. In order to ensure usability, configurations and management policies have to be maintained and deployed by cloud computing providers. Availability of cloud is defined as cloud computing platform provides stable and reliable computation resources to users. My research concentrates on improving usability and availability of cloud computing platforms. I proposed a customized agent-based reliability monitoring framework to increase reliability of cloud computing

Sistemas recomendadores aplicados en Educación

En este trabajo final integrador se analizaron diferentes técnicas de recomendación y se estudió su aplicabilidad en el ámbito educativo. Así también se presenta un resumen de las métricas usualmente utilizadas para medir la performance de éstos sistemas y cuáles son las variantes o nuevas métricas a tener en cuenta cuando se aplican éstos sistemas en educación. En el trabajo experimental se utilizaron diferentes conjuntos de datos de prueba abordados en la literatura de los SRE y se compararon los resultados obtenidos con distintos algoritmos de recomendación basados en la técnica de Filtrado Colaborativo (FC).Facultad de Informátic

Workshop Notes of the Sixth International Workshop "What can FCA do for Artificial Intelligence?"

International audienc