Towards an Integrative Educational Recommender for Lifelong Learners

One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents unique challenges, requiring scalable and transparent models that can account for learner knowledge and content novelty simultaneously, while also retaining accurate learners representations for long periods of time. We attempt to build a novel educational recommender, that relies on an integrative approach combining multiple drivers of learners engagement. Our first step towards this goal is TrueLearn, which models content novelty and background knowledge of learners and achieves promising performance while retaining a human interpretable learner model.Comment: In Proceedings of AAAI Conference on Artificial Intelligence 202

Timeliness of Materials on Reading Recommendation System

An improved fuzzy logic recommendation method named TFLRS is presented in this paper. The timeliness of reading materials is focused. The upload time of reading materials is attached as an important input parameter, and the numeric weights of input factors are further revised. The experiment result demonstrates that the recommendation ranking order of the latest and the out-of-date reading materials has obviously improved in comparison to the previous FLRS method. It solves the problem that the new reading materials cannot be timely discovered but the out-of-date reading materials always in the front of the recommendation ranking. The timeliness of reading materials effectively guarantees the user preferred newer materials are always at the higher level than the older materials in the recommendation ranking result and the accuracy of reading recommendation system has significantly improved

Implementasi Convolutional Neural Network dan Probabilistic Matrix Factorization pada Sistem Rekomendasi Buku

Sistem Rekomendasi dapat merekomendasikan buku pada user tertentu berdasarkan prediksi rating, isi konten buku, ataupun metode lainnya. Banyak metode recommendation system yang digunakan seperti Probabilistic Matrix Factorization, dimana konten yang sudah diberi rating akan sering direkomendasikan. Namun pada Probabilistic Matrix Factorization memiliki kekurangan yaitu dalam mengatasi data yang memiliki nilai rating yang jarang. Maka diperlukan suatu metode yang digunakan untuk memahami konteks isi dari buku sehingga tidak hanya melihat dari rating saja namun dilihat juga dari review suatu buku. Untuk mempelajari review maka diigunakan suatu metode yaitu Convolutional Neural Network dengan cara memberikan suatu nilai vektor yang mengarah terhadap konteks buku kepada Probabilistic Matrix Factorization suatu recommender system. Berdasarkan hasil pengujiannya, metode tersebut dapat meningkatkan keakuratan data dengan MAE = 3,0114707. Sedangkan untuk Probabilistic Matrix Factorization nilai MAE = 4,0185377. Dari nilai tersebut dapat dijelaskan bahwa metode Convolutional Neural Network dan Probabilistic Matrix Factorization bekerja cukup baik untuk data yang jarang memiliki rating

Recommending learning material in Intelligent Tutoring Systems

Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used

O Uso da Trajetória de Aprendizagem do Aluno em Ambientes Virtuais de Aprendizagem

Os Ambientes Virtuais de Aprendizagem produzem quantidades de dados significativas sobre a interação dos alunos com o ambiente. A trajetória da aprendizagem representa o caminho utilizado pelos alunos para alcançar os objetivos educacionais e pode ser obtida pela da análise desses dados. Através da análise da trajetória de aprendizagem, o professor pode retirar importantes informações sobre o comportamento dos alunos. O objetivo desse trabalho é realizar uma fundamentação da trajetória de aprendizagem, desde sua captura até a visualização, e descrever uma proposta de ferramenta para a análise da trajetória. Foram realizados levantamentos sobre as principais técnicas relacionadas à trajetória de aprendizagem e de trabalhos que realizaram esse tipo de análise. O resultado esperado é a criação de uma ferramenta para a captura e visualização de forma automática da trajetória que permita ao professor entender o comportamento de seus alunos

Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering

23 Halama

Personalised e-Learning

This thesis proposes to add value to the traditional e-learning systems by personalising the content being presented. The personalisation process was brought together through the amalgamation of crowdsourcing techniques, explicit with learners’ interests, and learner profiling technologies. A prototype called iPLE, intelligent personal learning environment, was developed and tested within an empirical study where participants experienced and compared the proposed iPLE with a static e-learning environment and a standard face-to-face delivery. A number of data collection instruments have been integrated within the empirical study to accumulate participants’ feedback. The results were fully documented and analysed using a combination of quantitative and qualitative data analysis tools that generated essential assessment information. An indicative improvement was reported following the data analysis and evaluation of results that led to the conclusion that even though there is plenty of room for further development and research, the combination of the proposed techniques does help and assist in rendering e-learning more effective

International Open and Distance Learning Conference proceedings book = Uluslararası Açık ve Uzaktan Öğrenme Konferansı bildiri kitabı

The 4th International Open & Distance Learning Conference- IODL 2019, which was held at Anadolu University, Eskişehir, Türkiye on 14-16 November, 2019