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336 results on '"Munoz-Merino, Pedro J."'

1. Statoodle: A Learning Analytics Tool to Analyze Moodle Students’ Actions and Prevent Cheating

Author

Moreno-Marcos, Pedro Manuel, Barredo, Jorge, Muñoz-Merino, Pedro J., Delgado Kloos, Carlos, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Viberg, Olga, editor, Jivet, Ioana, editor, Muñoz-Merino, Pedro J., editor, Perifanou, Maria, editor, and Papathoma, Tina, editor

Published
2023
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4. Leadership and Maturity: How Do They Affect Learning Analytics Adoption in Latin America? : A Cross-Case Analysis in Four Latin American Universities

Author

Hilliger, Isabel, Pérez-Sanagustín, Mar, Pérez-Álvarez, Ronald, Henríquez, Valeria, Guerra, Julio, Zuñiga-Prieto, Miguel Ángel, Ortiz-Rojas, Margarita, Tsai, Yi-Shan, Gasevic, Dragan, Muñoz-Merino, Pedro J., Broos, Tom, De Laet, Tinne, Ifenthaler, Dirk, Series Editor, and Gibson, David, Series Editor

Published
2020
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5. Should We Consider Efficiency and Constancy for Adaptation in Intelligent Tutoring Systems?

Author

Moreno-Marcos, Pedro Manuel, Martínez de la Torre, Dánae, González Castro, Gabriel, Muñoz-Merino, Pedro J., Delgado Kloos, Carlos, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kumar, Vivekanandan, editor, and Troussas, Christos, editor

Published
2020
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8. Prediction in MOOCs: A Review and Future Research Directions

Author

Moreno-Marcos, Pedro Manuel, Alario-Hoyos, Carlos, Munoz-Merino, Pedro J., and Kloos, Carlos Delgado

Abstract

This paper surveys the state of the art on prediction in MOOCs through a systematic literature review (SLR). The main objectives are: first, to identify the characteristics of the MOOCs used for prediction, second, to describe the prediction outcomes, third, to classify the prediction features, fourth, to determine the techniques used to predict the variables, and, fifth, to identify the metrics used to evaluate the predictive models. Results show there is strong interest in predicting dropouts in MOOCs. A variety of predictive models are used, though regression and support vector machines stand out. There is also wide variety in the choice of prediction features, but clickstream data about platform use stands out. Future research should focus on developing and applying predictive models that can be used in more heterogeneous contexts (in terms of platforms, thematic areas, and course durations), on predicting new outcomes and making connections among them (e.g., predicting learners' expectancies), on enhancing the predictive power of current models by improving algorithms or adding novel higher-order features (e.g., efficiency, constancy, etc.).

Published
2019
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9. Using Machine Learning to Detect 'Multiple-Account' Cheating and Analyze the Influence of Student and Problem Features

Author

Ruiperez-Valiente, Jose A., Munoz-Merino, Pedro J., Alexandron, Giora, and Pritchard, David E.

Abstract

One of the reported methods of cheating in online environments in the literature is CAMEO (Copying Answers using Multiple Existences Online), where harvesting accounts are used to obtain correct answers that are later submitted in the master account which gives the student credit to obtain a certificate. In previous research, we developed an algorithm to identify and label submissions that were cheated using the CAMEO method; this algorithm relied on the IP of the submissions. In this study, we use this tagged sample of submissions to i) compare the influence of student and problems characteristics on CAMEO and ii) build a random forest classifier that detects submissions as CAMEO without relying on IP, achieving sensitivity and specificity levels of 0.966 and 0.996, respectively. Finally, we analyze the importance of the different features of the model finding that student features are the most important variables towards the correct classification of CAMEO submissions, concluding also that student features have more influence on CAMEO than problem features.

Published
2019
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10. A Learning Analytics Methodology for Understanding Social Interactions in MOOCs

Author

Moreno-Marcos, Pedro Manuel, Alario-Hoyos, Carlos, Munoz-Merino, Pedro J., Estevez-Ayres, Iria, and Kloos, Carlos Delgado

Abstract

One of the characteristics of massive open online courses (MOOCs) is that the overall number of social interactions tend to be higher than in traditional courses, hindering the analysis of social learning. Learners typically ask or answer questions using the forum. This makes messages a rich source of information, which can be used to infer learners' behavior and outcomes. It is not feasible for teachers to process all forum messages and automated tools and analysis are required. Although there are some tools for analyzing learners' interactions, there is a need for methodologies and integrated tools that help to interpret the learning process based on social interactions in the forum. This paper presents the 3S (Social, Sentiments, Skills) learning analytics methodology for analyzing forum interactions in MOOCs. This methodology considers a temporal analysis combining the social, sentiments, and skill dimensions that can be extracted from forum data. We also introduce LAT[turned E]S, a learning analytics tool for edX/Open edX related to the three dimensions (3S), which includes visualizations to guide the proposed methodology. We apply the 3S methodology and the tool to an MOOC on Java programming. Results showed, among others, the action-reaction effect produced when learners increase their participation after instructor's events. Moreover, a decrease of positive sentiments over time and before deadlines of open-ended assignments was also observed and that there were certain skills, which caused more troubles (e.g., arrays and loops). These results acknowledge the importance of using a learning analytics methodology to detect problems in MOOCs.

Published
2019
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11. Principles for the Design of an Educational Voice Assistant for Learning Java

Author

Kloos, Carlos Delgado, Alario-Hoyos, Carlos, Muñoz-Merino, Pedro J., Aguirre, Cristina Catalán, Castro, Nuria González, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Pras, Aiko, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Furbach, Ulrich, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, and Mavengere, Nicholas, editor

Published
2019
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12. Analyzing the Group Formation Process in Intelligent Tutoring Systems

Author

Rubio-Fernández, Aarón, Muñoz-Merino, Pedro J., Kloos, Carlos Delgado, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Coy, Andre, editor, Hayashi, Yugo, editor, and Chang, Maiga, editor

Published
2019
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16. Predicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning

Author

Maldonado-Mahauad, Jorge, Pérez-Sanagustín, Mar, Moreno-Marcos, Pedro Manuel, Alario-Hoyos, Carlos, Muñoz-Merino, Pedro J., Delgado-Kloos, Carlos, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Pammer-Schindler, Viktoria, editor, Pérez-Sanagustín, Mar, editor, Drachsler, Hendrik, editor, Elferink, Raymond, editor, and Scheffel, Maren, editor

Published
2018
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17. The Effect of Different Features for Educational Computer-Based Competition Environments

Author

Munoz-Merino, Pedro J., Alario-Hoyos, Carlos, Munoz-Organero, Mario, Kloos, Carlos Delgado, and Molina, Manuel Fernandez

Abstract

Educational computer-based competition environments need to be designed with a set of features that enhance the learning process. Although recently some frameworks for the design of educational computer-based systems (e.g., educational games) have been proposed, they do not focus on the details of the competition. Therefore, the design of educational computer-based competition environments is still an open issue. We propose the 4NESS framework for the design of such competition environments. This framework classifies the competition features into four dimensions: easiness, fairness, awareness, and adaptiveness. We designed the ISCARE educational computer-based competition environment implementing these four dimensions to evaluate and validate some included features for competition, and conducted experiments during three consecutive years. Students considered the computer-based competition environment with the proposed features, including a modification of the Swiss system, to be very easy to use, fair for pairing students, moderately fair for calculating scores, with good awareness of their state during the competition and that of their peers, and with contents of appropriate level, especially for the group working with adaptive contents. Adaptation of contents made students increase their learning gains in 0.5 sigmas during the competition. Pairings were seen as with students of similar levels. And final scores were moderately fair: while the order tends to be similar as compared to the results in the post-test, students grades with high performance tend to be lower in the competition environment.

Published
2018
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18. Evaluating Student-Facing Learning Dashboards of Affective States

Author

Sedrakyan, Gayane, Leony, Derick, Muñoz-Merino, Pedro J., Kloos, Carlos Delgado, Verbert, Katrien, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lavoué, Élise, editor, Drachsler, Hendrik, editor, Verbert, Katrien, editor, Broisin, Julien, editor, and Pérez-Sanagustín, Mar, editor

Published
2017
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19. Early Prediction and Variable Importance of Certificate Accomplishment in a MOOC

Author

Ruipérez-Valiente, José A., Cobos, Ruth, Muñoz-Merino, Pedro J., Andujar, Álvaro, Delgado Kloos, Carlos, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Delgado Kloos, Carlos, editor, Jermann, Patrick, editor, Pérez-Sanagustín, Mar, editor, Seaton, Daniel T., editor, and White, Su, editor

Published
2017
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21. A Demonstration of ALAS-KA: A Learning Analytics Tool for the Khan Academy Platform

Author

Ruipérez-Valiente, José A., Muñoz-Merino, Pedro J., Kloos, Carlos Delgado, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Rensing, Christoph, editor, de Freitas, Sara, editor, Ley, Tobias, editor, and Muñoz-Merino, Pedro J., editor

Published
2014
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22. Do Optional Activities Matter in Virtual Learning Environments?

Author

Ruipérez-Valiente, José A., Muñoz-Merino, Pedro J., Kloos, Carlos Delgado, Niemann, Katja, Scheffel, Maren, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Rensing, Christoph, editor, de Freitas, Sara, editor, Ley, Tobias, editor, and Muñoz-Merino, Pedro J., editor

Published
2014
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25. Using Video Visualizations in Open edX to Understand Learning Interactions of Students

Author

Pijeira Díaz, Héctor J., Ruiz, Javier Santofimia, Ruipérez-Valiente, José A., Muñoz-Merino, Pedro J., Kloos, Carlos Delgado, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Conole, Gráinne, editor, Klobučar, Tomaž, editor, Rensing, Christoph, editor, Konert, Johannes, editor, and Lavoué, Elise, editor

Published
2015
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30. Educational Justifications for the Design of the ISCARE Computer Based Competition Assessment Tool

Author

Molina, Manuel Fernández, Muñoz-Merino, Pedro J., Muñoz-Organero, Mario, Kloos, Carlos Delgado, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Leung, Howard, editor, Popescu, Elvira, editor, Cao, Yiwei, editor, Lau, Rynson W. H., editor, and Nejdl, Wolfgang, editor

Published
2011
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31. Towards the Prediction of User Actions on Exercises with Hints Based on Survey Results

Author

Muñoz-Merino, Pedro J., Pardo, Abelardo, Muñoz-Organero, Mario, Delgado Kloos, Carlos, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Kloos, Carlos Delgado, editor, Gillet, Denis, editor, Crespo García, Raquel M., editor, Wild, Fridolin, editor, and Wolpers, Martin, editor

Published
2011
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32. Framework for Contextualized Learning Ecosystems

Author

Muñoz-Organero, Mario, Ramírez, Gustavo A., Muñoz-Merino, Pedro J., Kloos, Carlos Delgado, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Kloos, Carlos Delgado, editor, Gillet, Denis, editor, Crespo García, Raquel M., editor, Wild, Fridolin, editor, and Wolpers, Martin, editor

Published
2011
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33. Superpowers in the Classroom: Hyperchalk is an Online Whiteboard for Learning Analytics Data Collection

Author

Menzel, Lukas, Gombert, Sebastian, Mitri, Daniele Di, Drachsler, Hendrik, Hilliger, Isabel, Munoz-Merino, Pedro J., De Laet, Tinne, Ortega-Arranz, Alejandro, Farrell, Tracie, RS-Research Line Online Learning and Instruction (part of ERA program), and Department of Online Learning and Instruction

Abstract

In e-learning, collaborative online whiteboards have become a popular choice for implementing collaborative learning. However, existing solutions fail to deliver data in a way that allows for the application of learning analytics in this field. While the market offers several solutions, most of them cannot be integrated with existing learning management systems and do not provide data that can be used for learning analytics. To overcome this, we implement a digital collaborative whiteboard based on the open source Excalidraw and a custom back-end. The whiteboard can be self-hosted, it collects rich log data appropriate for learning analytics purposes and it integrates with learning management systems – such as Moodle – using the LTI 1.3 Advantage standard.

Published
2022

35. An Architecture for Combining Semantic Web Techniques with Intelligent Tutoring Systems

Author

Muñoz Merino, Pedro J., Kloos, Carlos Delgado, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Woolf, Beverley P., editor, Aïmeur, Esma, editor, Nkambou, Roger, editor, and Lajoie, Susanne, editor

Published
2008
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38. Behavior Effect of Hint Selection Penalties and Availability in an Intelligent Tutoring System

Author

Muñoz-Merino, Pedro J., Delgado Kloos, Carlos, Muñoz-Organero, Mario, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Aleven, Vincent, editor, Kay, Judy, editor, and Mostow, Jack, editor

Published
2010
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40. Programming Teaching Interaction

Author

Kloos, Carlos Delgado, primary, Fernandez-Panadero, Carmen, additional, Alario-Hoyos, Carlos, additional, Moreno-Marcos, Pedro Manuel, additional, Ibanez, Maria Blanca, additional, Munoz-Merino, Pedro J., additional, Garcia, Boni, additional, and Estevez-Ayres, Iria, additional

Published
2022
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42. Analyzing Learning Gains in a Competition Intelligent Tutoring System

Author

Muñoz-Merino, Pedro J., Delgado Kloos, Carlos, Fernández Molina, Manuel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Trausan-Matu, Stefan, editor, Boyer, Kristy Elizabeth, editor, Crosby, Martha, editor, and Panourgia, Kitty, editor

Published
2014
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44. Towards a Cloud-Based University Accelerated By the Pandemic

Author

Kloos, Carlos Delgado, primary, Alario-Hoyos, Carlos, additional, Fenandez-Panadero, Carmen, additional, Munoz-Merino, Pedro J., additional, Estevez-Ayres, Iria, additional, Muaoz-Organero, Mario, additional, Ibanez, Maria Blanca, additional, Moreno-Marcos, Pedro Manuel, additional, and Garcia, Boni, additional

Published
2021
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45. Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics

Author

Moreno-Marcos, Pedro Manuel, Pong, Ting Chuen, Munoz-Merino, Pedro J., Delgado Kloos, Carlos, Moreno-Marcos, Pedro Manuel, Pong, Ting Chuen, Munoz-Merino, Pedro J., and Delgado Kloos, Carlos

Abstract

The advancement of learning analytics has enabled the development of predictive models to forecast learners' behaviors and outcomes (e.g., performance). However, many of these models are only applicable to specific learning environments and it is usually difficult to know which factors influence prediction results, including the predictor variables as well as the type of prediction outcome. Knowing these factors would be relevant to generalize to other contexts, compare approaches, improve the predictive models and enhance the possible interventions. In this direction, this work aims to analyze how several factors can make an influence on the prediction of students' performance. These factors include the effect of previous grades, forum variables, variables related to exercises, clickstream data, course duration, type of assignments, data collection procedure, question format in an exam, and the prediction outcome (considering intermediate assignment grades, including the final exam, and the final grade). Results show that variables related to exercises are the best predictors, unlike variables about forum, which are useless. Clickstream data can be acceptable predictors when exercises are not available, but they do not add prediction power if variables related to exercises are present. Predictive power was also better for concept-oriented assignments and best models usually contained only the last interactions. In addition, results showed that multiple-choice questions were easier to predict than coding questions, and the final exam grade (actual knowledge at a specific moment) was harder to predict than the final grade (average knowledge in the long term), based on different assignments during the course. © 2013 IEEE.

Published
2020

46. Making Educational Technology Invisible

Author

Kloos, Carlos Delgado, primary, Alario-Hoyos, Carlos, additional, Munoz-Merino, Pedro J., additional, Ibanez, Mo Blanca, additional, Fernandez-Panadero, Carmen, additional, and Estevez-Ayres, Iria, additional

Published
2020
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