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

Applied clustering analysis for grouping behaviour of e-learning usage based on meaningful learning characteristics

One of the critical success factors of e-learning is positive interest of students towards e-learning. The majority of activities of current e-learning usage are viewing and downloading. These activities are not meaningful with regard to enhancing learning quality. Due to that, the aim of this paper is to analyze students’ usage based on meaningful learning characteristics by clustering students’ activities and actions during online learning. We first define meaningful learning characteristics (as those which are active, authentic, cooperative, collaborative, and intentional) and associate these with e-learning activities and actions. Then, we analyze the students’ e-learning usage and define the cluster of student’s meaningful characteristics by using the K-Means cluster method. A case study has been conducted based on the e-learning log files of 37 students on Computational Intelligence Course at the Software Engineering Department, Universiti Teknologi Malaysia. The result of this clustering enables us to determine the students with high ratings on these meaningful activities and actions during online learning. We found out that students with high hits on add, update, and edit are included in the high cluster group. On the contrary, students with high hits on the view actions for all e-learning activities are included in the low cluster group. This result may assist instructors while preparing the strategy of computer usage for education, in terms of providing a greater variety of learning activities, which is applicable for any courses

Exploring Multi-Level Parallelism For Graph-Based Applications Via Algorithm And System Co-Design

Graph processing is at the heart of many modern applications where graphs are used as the basic data structure to represent the entities of interest and the relationships between them. Improving the performance of graph-based applications, especially using parallelism techniques, has drawn significant interest both in academia and industry. On the one hand, modern CPU architectures are able to provide massive computational power by using sophisticated memory hierarchy and multi-level parallelism, including thread-level parallelism, data-level parallelism, etc. On the other hand, graph processing workloads are notoriously challenging for achieving high performance due to their irregular computation pattern and unpredictable control flow. Therefore, how to accelerate the performance of graph-based applications using parallelism is still an open question. This dissertation focuses on providing high performance for graph-based applications. To take full advantage of multi-level parallelism resources provided by CPUs, this dissertation studies the characteristics of graph-based applications and matches their parallel solutions with the underlying hardware via algorithm and system co-design. This dissertation divides graph-based applications into three categories: typical graph algorithms, sequential graph-based applications, and applications with graph-based solutions. The first category comprises typical graph algorithms with available parallel solutions. This dissertation proposes GraphPhi as a new approach to graph processing on emerging Intel Xeon Phi-like architectures. The second category includes specialized graph applications without nontrivial parallel solutions. This dissertation studies a state-of-the-art 2-hop labeling approach named Pruned Landmark Labeling (PLL). This dissertation proposes Batched Vertex-Centric PLL (BVC-PLL), which breaks PLL\u27s inherent dependencies and parallelizes it in a scalable way. The third category includes applications that rely on graph-based solutions. This dissertation studies the sequential search algorithm for the graph-based indexing methods used for the Approximate Nearest Neighbor Search (ANNS) problem. This dissertation proposes Speed-ANN, a parallel similarity search algorithm that reveals hidden intra-query parallelism to accelerate the search speed while fulfilling the high accuracy requirement. Moreover, this dissertation further explores the optimization opportunities for computational graph-based deep neural network inference running on tiny devices, specifically microcontrollers (MCUs). Altogether, this dissertation studies graph-based applications and improves their performance by providing solutions of multi-level parallelism via algorithm and system co-design to match them with the underlying multi-core CPU architectures