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Ana-Elena Guerrero-Roldán

Bio: Ana-Elena Guerrero-Roldán is an academic researcher from Open University of Catalonia. The author has contributed to research in topics: Educational technology & Virtual learning environment. The author has an hindex of 11, co-authored 44 publications receiving 355 citations.


Papers
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Journal ArticleDOI
TL;DR: The results of the study indicate that agile strategies are useful for improving students' online project management and collaboration and no significant impact has been observed in students' satisfaction nor in the overall learning outcomes.
Abstract: Unsatisfactory prior experiences in collaborative learning influence students' predisposition towards team-based learning activities. Incorporating strategies for helping teams to effectively regulate group work and enhance planning processes may result in an increase in students' engagement with learning activities and collaborative processes. Taking into account the benefits of the agile method for teamwork organisation, this study sought to analyse the usefulness of agile strategies for team regulation and project management in online higher education. An iterative process of course redesign was conducted in the context of an undergraduate project-based learning course during two consecutive semesters. The new design was piloted and evaluated based on the students' and teacher's views and the learning outcomes. A total of 114 students were surveyed about their satisfaction with the course and their perception of the usefulness of the method. Two interviews were conducted to collect the teacher's opinions. The results of the study indicate that agile strategies are useful for improving students' online project management and collaboration. Nevertheless, no significant impact has been observed in students' satisfaction nor in the overall learning outcomes.

82 citations

Journal ArticleDOI
TL;DR: The model developed takes advantage of the potential for technologies to go beyond traditional assessment approaches and proposes a classification of e-assessment activities organized by competences, which can help teachers and students better understand the meaning of competence-based learning.
Abstract: This article presents a model for designing e-assessment processes aligned with competences and learning activities. The authors examined assessment in student-centered, competence-based learning in online contexts. We analyzed the importance of alignment for properly selecting the learning activities that best guide students towards the desired level of competence acquisition (i.e. learning outcomes). We explored the leading types of assessment and new opportunities for assessment derived from the use of technologies. The model developed takes advantage of the potential for technologies to go beyond traditional assessment approaches and proposes a classification of e-assessment activities organized by competences. When the model was applied in a real online course, results suggested it can help teachers and students better understand the meaning of competence-based learning and how the formative assessment approach is useful for helping students attain the desired competence levels.

55 citations

Journal ArticleDOI
TL;DR: In this article, the authors examined AI studies in education in half a century (1970-2020) through a systematic review approach and benefits from social network analysis and text-mining approaches, and identified three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues.
Abstract: Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

47 citations

Journal ArticleDOI
TL;DR: The proposed machine-learning based framework learns students’ patterns of language use from data, providing an accessible and non-invasive validation of student identities and student-produced content to enhance academic integrity in the e-learning environment.
Abstract: This paper presents the results of integrating learning analytics into the assessment process to enhance academic integrity in the e-learning environment. The goal of this research is to evaluate the computational-based approach to academic integrity. The machine-learning based framework learns students’ patterns of language use from data, providing an accessible and non-invasive validation of student identities and student-produced content. To assess the performance of the proposed approach, we conducted a series of experiments using written assignments of graduate students. The proposed method yielded a mean accuracy of 93%, exceeding the baseline of human performance that yielded a mean accuracy rate of 12%. The results suggest a promising potential for developing automated tools that promote accountability and simplify the provision of academic integrity in the e-learning environment.

47 citations

Journal ArticleDOI
TL;DR: An early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education.
Abstract: Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

36 citations


Cited by
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Journal ArticleDOI
TL;DR: Doing qualitative research: a practical handbook, by David Silverman, Los Angeles, Sage, 2010, 456 pp., AU$65.00, ISBN 978-1-84860-033-1, ISBN 1-94960-034-8 as mentioned in this paper.
Abstract: Doing qualitative research: a practical handbook, by David Silverman, Los Angeles, Sage, 2010, 456 pp., AU$65.00, ISBN 978-1-84860-033-1, ISBN 978-1-94960-034-8. Available in Australia and New Zeal...

2,295 citations

Book
01 Jan 2003

911 citations

17 Dec 2010
TL;DR: The authors survey the vast terrain of "culturomics", focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000, using a corpus of digitized texts containing about 4% of all books ever printed.
Abstract: L'article, publie dans Science, sur une des premieres utilisations analytiques de Google Books, fondee sur les n-grammes (Google Ngrams) We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of "culturomics", focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can ...

735 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide an overview of research on AI applications in higher education through a systematic review, focusing on four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, adaptive systems and personalisation, and 4. intelligent tutoring systems.
Abstract: According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

520 citations

Journal ArticleDOI
TL;DR: In this paper, a systematic literature review (SLR) was conducted to identify the research topics, most relevant theories, most researched modalities, and the research methodologies used for e-learning.
Abstract: The concept of e-learning is a technology-mediated learning approach of great potential from the educational perspective and it has been one of the main research lines of Educational Technology in the last decades The aim of the present systematic literature review (SLR) was to identify (a) the research topics; (b) the most relevant theories; (c) the most researched modalities; and (d) the research methodologies used To this end, the PRISMA protocol was followed, and different tools were used for the bibliographic management and text-mining The literature selection was carried out in three first-quartile journals indexed in JCR-SSCI specialized in Educational Technology A total of 248 articles composed the final sample The analysis of the texts identified three main nodes: (a) online students; (b) online teachers; and (c) curriculum-interactive learning environments It was revealed that MOOC was the most researched e-learning modality The Community of Inquiry and the Technological Acceptance Model, were the most used theories in the analyzed studies The most frequent methodology was case study Finally, the conclusions regarding the objectives of our SRL are presented: Main themes and research sub-themes, most researched e-learning modality, most relevant theoretical frameworks on e-learning, and typologies of research methodologies

216 citations