TOWARDS FORMATIVE E-ASSESSMENT IN PROJECT MANAGEMENT THROUGH PERSONALIZED AUTOMATED FEEDBACK

Formative e-assessment is a complex process, in which learners can build their knowledge, fill up their knowledge gaps or increase their learning abilities. The feedback mechanism is considered to be highly important for the formative dimension of e-assessment. Current paper proposes a model for automated feedback in a project management e-assessment environment: the model blends a built-in feedback sheet (a document containing the correct answers) with a recommender engine, which searches the web for references related to the incorrectly answered questions. The feedback model is personalized, because the web search is made taking into account the user profile: the list of concepts which weren’t correctly understood. This list of concepts is mapped on project management domain ontology.e-assessment, project management, automated feedback, ontology, knowledge system

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Scalable and interpretable product recommendations via overlapping co-clustering

We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201

A Personalized Knowledge Recommender System For Workspace Learning

Technology Enhanced Learning (TEL) is emerging as a popular learning approach utilized by both educational institutions and business organizations. Learning Recommender Systems (RSs) can help e-learners to cope with the data overload difficulty and suggest useful items that users may wish to use. This research aims to examine the design and implementation of personalized RS that supports individual learning in the workplace. First, a hybrid knowledge recommendation technique is proposed by combing content-based method with feedback learning method to adapt to the dynamic preference of users. Second, the design and implementation of a personalized knowledge recommender system using proposed technique in a case company is presented. Quantitative and qualitative data are collected to validate the system and evaluate its performance and impact. The preliminary results show that involving enterprise experts and target users in the system design phase can improve the system transparency and users’ trust in the system. It is also found that users’ learning attitude can be positively influenced by the system experience. This research provides important implications on employing intelligent recommender system to support workplace learning

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Trust and Distrust in Big Data Recommendation Agents

Big data technology allows for managing data from a variety of sources, in large amounts, and at a higher velocity than before, impacting several traditional systems, including recommendation agents. Along with these improvements, there are concerns about trust and distrust in RA recommendations. Much prior work on trust has been done in IS, but only a few have examined trust and distrust in the context of big data and analytics. In this vein, the purpose of this study is to study the eight antecedents of trust and distrust in recommendation agents’ cues in the context of the Big Data ecosystem using an experiment. Our study contributes to the literature by integrating big data and recommendation agent IT artifacts, expanding trust and distrust theory in the context of a big data ecosystem, and incorporating the constructs of algorithm innovativeness and process transparency