Salvador Sánchez-Alonso

Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content

Online reviews have received much attention recently in the literature, as their visibility has been proven to play an important role during the purchase process. Furthermore, recent theoretical insight argue that the votes casted on how helpful an online review is (review helpfulness) are of particular importance, since they constitute a focal point for examining consumer decision making during the purchase process. In this paper, we explore the interplay between online review helpfulness, rating score and the qualitative characteristics of the review text as measured by readability tests. We construct a theoretical model based on three elements: conformity, understandability and expressiveness and we investigate the directional relationship between the qualitative characteristics of the review text, review helpfulness and the impact of review helpfulness on the review score. Furthermore, we examine whether this relation holds for extreme and moderate review scores. To validate this model we applied four basic readability measures to a dataset containing 37,221 reviews collected from Amazon UK, in order to determine the relationship between the percentage of helpful votes awarded to a review and the review texts stylistic elements. We also investigated the interrelationships between extremely helpful and unhelpful reviews, as well as absolutely positive and negative reviews using intergroup comparisons. We found that review readability had a greater effect on the helpfulness ratio of a review than its length; in addition, extremely helpful reviews received a higher score than those considered less helpful. The present study contributes to the ever growing literature on on-line reviews by showing that readability tests demonstrate a directional relationship with average length reviews and their helpfulness and that this relationship holds both for moderate and extreme review scores.

Interlinking educational resources and the web of data: A survey of challenges and approaches

Purpose - Research in the area of technology-enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, or interface mechanisms. More recently, semantic technologies were taken into account to improve interoperability. The linked data approach has emerged as the de facto standard for sharing data on the web. To this end, it is obvious that the application of linked data principles offers a large potential to solve interoperability issues in the field of TEL. This paper aims to address this issue. Design/methodology/approach - In this paper, approaches are surveyed that are aimed towards a vision of linked education, i.e. education which exploits educational web data. It particularly considers the exploitation of the wealth of already existing TEL data on the web by allowing its exposure as linked data and by taking into account automated enrichment and interlinking techniques to provide rich and well-interlinked data for the educational domain. Findings - So far web-scale integration of educational resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. However, linked data principles increasingly are recognized by the TEL community. The paper provides a structured assessment and classification of existing challenges and approaches, serving as potential guideline for researchers and practitioners in the field. Originality/value - Being one of the first comprehensive surveys on the topic of linked data for education, the paper has the potential to become a widely recognized reference publication in the area.

Evaluating collaborative filtering recommendations inside large learning object repositories

Collaborative filtering (CF) algorithms are techniques used by recommender systems to predict the utility of items for users based on the similarity among their preferences and the preferences of other users. The enormous growth of learning objects on the internet and the availability of preferences of usage by the community of users in the existing learning object repositories (LORs) have opened the possibility of testing the efficiency of CF algorithms on recommending learning materials to the users of these communities. In this paper we evaluated recommendations of learning resources generated by different well known memory-based CF algorithms using two databases (with implicit and explicit ratings) gathered from the popular MERLOT repository. We have also contrasted the results of the generated recommendations with several existing endorsement mechanisms of the repository to explore possible relations among them. Finally, the recommendations generated by the different algorithms were compared in order to evaluate whether or not they were overlapping. The results found here can be used as a starting point for future studies that account for the specific context of learning object repositories and the different aspects of preference in learning resource selection.

Computing with competencies: Modelling organizational capacities

The notion of competency provides an observable account of concrete human capacities under specific work conditions. The fact that competencies are subject to concrete kinds of measurement entails that they are subject to some extent to comparison and even in some sense, calculus. Then, competency models and databases can be used to compute competency gaps, to aggregate competencies of individuals as part of groups, and to compare capacities. However, as of today there is not a commonly agreed model or ontology for competencies, and scattered reports use different models for computing with competencies. This paper addresses how computing with competencies can be approached from a general perspective, using a flexible and extensible ontological model that can be adapted to the particularities of concrete organizations. Then, the consideration of competencies as an organizational asset is approached from the perspective of particular issues as competency gap analysis, the definition of job positions and how learning technology can be linked with competency models. The framework presented provides a technology-based baseline for organizations dealing with competency models, enabling the management of the knowledge acquisition dynamics of employees as driven by concrete and measurable accounts of organizational needs.

Exploring user-based recommender results in large learning object repositories: the case of MERLOT

Collaborative filtering (CF) techniques have proved to be effective in their application to e-commerce and other application domains. However, their applicability to the recommendation of learning resources deserve separate attention as seeking learning resources can be hypothesized to be substantially different from selecting information resources or products for purchase. To date there are only a few scattered studies reporting on the application of well known user-based CF algorithms to learning object repositories. This paper reports an empirical study carried out by using MERLOT data and existing user-based CF algorithms. The aim of this preliminary study was that of finding evidence on accuracy measures of existing CF algorithms, and the relation of the items recommended with other elements of the repository. The results can be used as a starting point for future studies that account for the specific context of learning object repositories and the different aspects of preference in learning resource selection.

Semantic learning object repositories

Current approaches towards enhancing reusability of learning materials agree that the concept of learning object is the key element of a new approach based on the creation of distributed repositories. In this new approach, learning objects can be accessed, searched and retrieved, making use of the information in their associated metadata records. The existence of different learning object definitions asks for a new generation of flexible repositories where all the existent conceptualisations can coexist. Ontological representations play, in this new generation of repositories, an important role as the basic support for sound semantic models that fulfil the new requirements. This paper introduces the main ideas about a new generation of flexible repositories, aimed at fitting all the definitions of learning object: the semantic repository.

Metrics-based evaluation of learning object reusability

This paper aims to help in the selection of reusable educational materials from repositories on the web, developing an indicator of the reusability of learning objects. For this purpose, our research will be carried out in three stages. The first, based on previous studies in this area, will determine those aspects that influence reusability. The second will define a set of metrics that measure those aspects using metadata. The third will propose different methods of aggregation in order to obtain a single resulting value and evaluate the efficiency of the model by analyzing a significant set of learning objects obtained from the eLera and Merlot repositories. The results obtained suggest that the proposed indicator could provide useful information when searching for learning objects in repositories. This reusability measurement could constitute an indicator of quality, which would allow search results to be ordered, with those with the greatest possibility of being reused taking priority. Furthermore, the proposed reusability indicator could be calculated automatically or in an assisted way if metadata elements satisfy the minimum quality requisites identified.

Architecture of the Organic.Edunet Web Portal

The use of Semantic Web technologies in educational Web portals has been reported to facilitate users search, access, and retrieval of learning resources. To achieve this, a number of different architectural components and services need to be harmonically combined and implemented. This article presents how this issue is dealt with in the context of a large-scale case study. More specifically, it describes the architecture behind the Organic. Edunet Web portal that aims to provide access to a federation of repositories with learning resources on agricultural topics. The various components of the architecture are presented and the supporting technologies are explained. In addition, the article focuses on how Semantic Web technologies are being adopted, specialized, and put in practice in order to facilitate ontology-aided sharing and reusing of learning resources.

Ranking Learning Objects through Integration of Different Quality Indicators

The solutions used to-date for recommending learning objects have proved unsatisfactory. In an attempt to improve the situation, this document highlights the insufficiencies of the existing approaches, and identifies quality indicators that might be used to provide information on which materials to recommend to users. Next, a synthesized quality indicator that can facilitate the ranking of learning objects, according to their overall quality, is proposed. In this way, explicit evaluations carried out by users or experts will be used, along with the usage data; thus, completing the information on which the recommendation is based. Taking a set of learning objects from the Merlot repository, we analyzed the relationships that exist between the different quality indicators to form an overall quality indicator that can be calculated automatically, guaranteeing that all resources will be rated.

A usability study of taxonomy visualisation user interfaces in digital repositories

Purpose – This paper aims to analyse user interfaces for search and collection visualisation and navigation from a usability perspective. The final aim is to offer repository owners a scientific basis to support their decisions when they have to choose an interface that can really help users to effectively locate and visualise resources over large digital collections. Design/methodology/approach – This HCI study is divided into two parts: perception and usability. The first one analysed three perceptual abilities required to use interfaces: attention, retention of information and understanding. The second one was run on an ad hoc generated collection including more than 40,000 European digital resources collected and classified according to a given branch of knowledge in the Art & Architecture Thesaurus. Findings – Although visual interfaces proved useful for certain tasks related to resource discovery and search, and despite the overall good general user opinion, the authors found it necessary to conduct...