1,720,963 research outputs found
Explainable Prediction of Student Performance in Online Courses
No full textStudent Performance Prediction (SPP) models and tools are useful for quickly identifying at-risk students in online courses and enable the provision of personalized learning plans and assistance. Additionally, they give educators and course managers the information they need to recognize the programs that require improvement. High accuracy is essential for such tools, but understanding the reasons of their predictions is equally important to ensure fairness and build trust in their adoption. Although many SPP models and tools have been proposed so far by different researchers, very few of them take explainability into account. This research proposes an SPP approach that is both effective and explainable. Based on demographic, administrative, engagement, and intra-course outcome data, it enables the prediction of student performance in terms of success/failure and final grade. It supports multiple machine learning models and includes post-hoc techniques for explainability capable of justifying the behavior of the whole system as well as its individual predictions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
An Architectural System for Automatic Pedagogical Interventions in Massive Online Learning Environments
No full textMassive Open Online Courses (MOOCs) are a teaching method that uses Virtual Learning Environments to reach a vast number of students, thus, facilitating access to education by making costs more appealing because of scale economics. Consequently, Tutors’ and teachers’ interaction is crucial for the successful development of a MOOC. However, due to the size and diversity of the student body in MOOCs, instructors and tutors need help to keep an eye on them carefully and intervene as needed. This work aims to set and validate an architecture for pedagogical interventions in online learning based on how a student feels, using the automatically detected subjective attributes obtained through interactions in the learning management systems. The architecture is based on three layers: (i) the Application layer for managing interaction with the Virtual Learning Environment; (ii) the Knowledge layer for the automatic textual classification, the attributes identification, knowledge representation through ontology and selection of pedagogical intervention actions; and (iii) the Intervention layer carries out pedagogical interventions through an autonomous conversational agent. The proposed architecture can identify the necessary pedagogical intervention, and the conversational agent can make decisions and adopt an approach more suited to the student’s needs. The proposed architecture was evaluated using the Stanford MOOC dataset, comprised of 11,042 participants who posted 29,604 messages from eleven courses. The preliminary evaluation results conclude that our approach is able to significantly support the tutor in MOOC environments as 65% of the student posts were automatically managed by the system while only the 35% left needed tutor attention
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
CAERS: A Conversational Agent for Intervention in MOOCs' Learning Processes
No full textMassive Open Online Courses (MOOCs) make up a teaching modality that aims to reach a large number of students using Virtual Learning Environments. In these courses, the intervention of tutors and teachers is essential to support students in the teaching-learning process, answer questions about their content, and provide engagement for students. However, as these courses have a vast and diverse audience, tutors and teachers find it difficult to monitor them closely and efficiently with prompt interventions. This work proposes an architecture to favor the construction of knowledge for students, tutors, and teachers through autonomous interference and recommendations of educational resources. The architecture is based on a conversational agent and an educational recommendation system. For the training of predictive models and extraction of semantic information, ontology and logical rules were used, together with inference algorithms and machine learning techniques, which act on a dataset with messages exchanged between course forum participants in the humanities, medicine, and education fields. The messages are classified according to the type (question, answer, and opinion) and parameters about feeling, confusion, and urgency. The architecture can infer the moment in which a student needs help and, through a Conversational Recommendation System, provides the student with the opportunity to revise his or her knowledge on the subject. To help in this task, the architecture can provide educational resources via an autonomous agent, contributing to reducing the degree of confusion and urgency identified in the posts. Initial results indicate that integrating technologies and resources, complementing each other, can support the students and help them succeed in their educational training
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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