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Pedro Manuel Moreno-Marcos
Department of Telematics Engineering, Universidad Carlos III de Madrid, E-28911 Leganés, Spain

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Journal article
Published: 20 August 2021 in Sustainability
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Discussion forums are a valuable source of information in educational platforms such as Massive Open Online Courses (MOOCs), as users can exchange opinions or even help other students in an asynchronous way, contributing to the sustainability of MOOCs even with low interaction from the instructor. Therefore, the use of the forum messages to get insights about students’ performance in a course is interesting. This article presents an automatic grading approach that can be used to assess learners through their interactions in the forum. The approach is based on the combination of three dimensions: (1) the quality of the content of the interactions, (2) the impact of the interactions, and (3) the user’s activity in the forum. The evaluation of the approach compares the assessment by experts with the automatic assessment obtaining a high accuracy of 0.8068 and Normalized Root Mean Square Error (NRMSE) of 0.1799, which outperforms previous existing approaches. Future research work can try to improve the automatic grading by the training of the indicators of the approach depending on the MOOCs or the combination with text mining techniques.

ACS Style

Raquel L. Pérez-Nicolás; Carlos Alario-Hoyos; Iria Estévez-Ayres; Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Carlos Delgado Kloos. Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums. Sustainability 2021, 13, 9364 .

AMA Style

Raquel L. Pérez-Nicolás, Carlos Alario-Hoyos, Iria Estévez-Ayres, Pedro Manuel Moreno-Marcos, Pedro J. Muñoz-Merino, Carlos Delgado Kloos. Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums. Sustainability. 2021; 13 (16):9364.

Chicago/Turabian Style

Raquel L. Pérez-Nicolás; Carlos Alario-Hoyos; Iria Estévez-Ayres; Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Carlos Delgado Kloos. 2021. "Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums." Sustainability 13, no. 16: 9364.

Research article
Published: 07 April 2021 in Computer Applications in Engineering Education
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Over decades, Mechanical Engineering students often find some difficulties to grasp some contents and/or struggle with some parts of the course. With the increasing development of new technologies, promising innovations can be implemented enhancing learning and improving success rates. In this study, a new learning interactive method is proposed and evaluated using the experience of over 600 students of Mechanical Engineering. This study describes a 4‐year experiment based on new interactive applications for education. The experiment has been implemented using E‐learning techniques and new technologies (a combination of remote and virtual examples, videos, quizzes, and theory). Specifically, several applications have been programmed to be executed on different devices, such as mobile phones and PC/laptops (Android and Windows). The experiment is applied using small applications that help the students identify the most challenging contents and guide them throughout step‐by‐step. The main objective of this interactive method is to help students find their lack of knowledge and offer them contents to cover it. These didactic applications are portable and intuitive. Thanks to these interactive applications, it is possible to accomplish better practices of “E‐learning” and “Computer Simulation and Animation” together. Since they are portable applications, they allow the student to interact and check conceptual understandings at any place. Students really appreciate this aspect. The results of the course titled Mechanism and Machine Theory have been analyzed during these four last years in which these interactive applications have been offered to the students.

ACS Style

Eduardo Corral Abad; María J. Gómez García; Efren Diez‐Jimenez; Pedro M. Moreno‐Marcos; Cristina Castejón Sisamon. Improving the learning of engineering students with interactive teaching applications. Computer Applications in Engineering Education 2021, 1 .

AMA Style

Eduardo Corral Abad, María J. Gómez García, Efren Diez‐Jimenez, Pedro M. Moreno‐Marcos, Cristina Castejón Sisamon. Improving the learning of engineering students with interactive teaching applications. Computer Applications in Engineering Education. 2021; ():1.

Chicago/Turabian Style

Eduardo Corral Abad; María J. Gómez García; Efren Diez‐Jimenez; Pedro M. Moreno‐Marcos; Cristina Castejón Sisamon. 2021. "Improving the learning of engineering students with interactive teaching applications." Computer Applications in Engineering Education , no. : 1.

Journal article
Published: 24 December 2020 in Applied Sciences
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MOOCs (massive open online courses) have a built-in forum where learners can share experiences as well as ask questions and get answers. Nevertheless, the work of the learners in the MOOC forum is usually not taken into account when calculating their grade in the course, due to the difficulty of automating the calculation of that grade in a context with a very large number of learners. In some situations, discussion forums might even be the only available evidence to grade learners. In other situations, forum interactions could serve as a complement for calculating the grade in addition to traditional summative assessment activities. This paper proposes an algorithm to automatically calculate learners’ grades in the MOOC forum, considering both the quantitative dimension and the relevance in their contributions. In addition, the algorithm has been implemented within a web application, providing instructors with a visual and a numerical representation of the grade for each learner. An exploratory analysis is carried out to assess the algorithm and the tool with a MOOC on programming, obtaining a moderate positive correlation between the forum grades provided by the algorithm and the grades obtained through the summative assessment activities. Nevertheless, the complementary analysis conducted indicates that this correlation may not be enough to use the forum grades as predictors of the grades obtained through summative assessment activities.

ACS Style

Sergio García-Molina; Carlos Alario-Hoyos; Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Iria Estévez-Ayres; Carlos Delgado Kloos. An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion Forum. Applied Sciences 2020, 11, 95 .

AMA Style

Sergio García-Molina, Carlos Alario-Hoyos, Pedro Manuel Moreno-Marcos, Pedro J. Muñoz-Merino, Iria Estévez-Ayres, Carlos Delgado Kloos. An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion Forum. Applied Sciences. 2020; 11 (1):95.

Chicago/Turabian Style

Sergio García-Molina; Carlos Alario-Hoyos; Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Iria Estévez-Ayres; Carlos Delgado Kloos. 2020. "An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion Forum." Applied Sciences 11, no. 1: 95.

Journal article
Published: 15 December 2020 in The Internet and Higher Education
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The purpose of this paper is to explore the expectations of academic staff to learning analytics services from an ideal as well as a realistic perspective. This mixed-method study focused on a cross-case analysis of staff from Higher Education Institutions from four European universities (Spain, Estonia, Netherlands, UK). While there are some differences between the countries as well as between ideal and predicted expectations, the overarching results indicate that academic staff sees learning analytics as a tool to understand the learning activities and possibility to provide feedback for the students and adapt the curriculum to meet learners' needs. However, one of the findings from the study across cases is the generally consistently low expectation and desire for academic staff to be obligated to act based on data that shows students being at risk of failing or under-performing.

ACS Style

Kaire Kollom; Kairit Tammets; Maren Scheffel; Yi-Shan Tsai; Ioana Jivet; Pedro J. Muñoz-Merino; Pedro Manuel Moreno-Marcos; Alexander Whitelock-Wainwright; Adolfo Ruiz Calleja; Dragan Gasevic; Carlos Delgado Kloos; Hendrik Drachsler; Tobias Ley. A four-country cross-case analysis of academic staff expectations about learning analytics in higher education. The Internet and Higher Education 2020, 49, 100788 .

AMA Style

Kaire Kollom, Kairit Tammets, Maren Scheffel, Yi-Shan Tsai, Ioana Jivet, Pedro J. Muñoz-Merino, Pedro Manuel Moreno-Marcos, Alexander Whitelock-Wainwright, Adolfo Ruiz Calleja, Dragan Gasevic, Carlos Delgado Kloos, Hendrik Drachsler, Tobias Ley. A four-country cross-case analysis of academic staff expectations about learning analytics in higher education. The Internet and Higher Education. 2020; 49 ():100788.

Chicago/Turabian Style

Kaire Kollom; Kairit Tammets; Maren Scheffel; Yi-Shan Tsai; Ioana Jivet; Pedro J. Muñoz-Merino; Pedro Manuel Moreno-Marcos; Alexander Whitelock-Wainwright; Adolfo Ruiz Calleja; Dragan Gasevic; Carlos Delgado Kloos; Hendrik Drachsler; Tobias Ley. 2020. "A four-country cross-case analysis of academic staff expectations about learning analytics in higher education." The Internet and Higher Education 49, no. : 100788.

Conference paper
Published: 03 June 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Intelligent Tutoring Systems (ITSs) usually make adaptation decisions based on user models that rely on students’ knowledge. However, there are other interesting indicators, which could be used for adaptation that need further exploration. Students’ efficiency (defined as whether they require a lot of time to achieve correctness in their exercises) and constancy (defined as whether they spend a similar time each day they take exercises in the ITS) are two of these indicators. This work aims to analyze 1) how these variables are distributed among students, 2) their evolution over time, and 3) how they are related to other outcomes. Results show that there are different profiles based on the efficiency; e.g., students with low efficiency that need a lot of time to solve exercises correctly, and low reflective students, among others. Furthermore, efficiency and constancy do not vary on average throughout the course. In addition, students are less constant in their daily time spent when their total time spent and average time per exercise is higher, and more efficient students tend to be more constant. Finally, it was found that neither efficiency nor constancy correlate with better grades. The existence of different profiles based on these variables and that they add a different dimension from student knowledge based on answer on exercises suggest that ITSs can make adaptation based on efficiency and constancy.

ACS Style

Pedro Manuel Moreno-Marcos; Dánae Martínez de la Torre; Gabriel González Castro; Pedro J. Muñoz-Merino; Carlos Delgado Kloos. Should We Consider Efficiency and Constancy for Adaptation in Intelligent Tutoring Systems? Transactions on Petri Nets and Other Models of Concurrency XV 2020, 237 -247.

AMA Style

Pedro Manuel Moreno-Marcos, Dánae Martínez de la Torre, Gabriel González Castro, Pedro J. Muñoz-Merino, Carlos Delgado Kloos. Should We Consider Efficiency and Constancy for Adaptation in Intelligent Tutoring Systems? Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():237-247.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Dánae Martínez de la Torre; Gabriel González Castro; Pedro J. Muñoz-Merino; Carlos Delgado Kloos. 2020. "Should We Consider Efficiency and Constancy for Adaptation in Intelligent Tutoring Systems?" Transactions on Petri Nets and Other Models of Concurrency XV , no. : 237-247.

Journal article
Published: 20 May 2020 in Computers & Education
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Learning analytics (LA) as a research field has grown rapidly over the last decade. However, adoption of LA is mostly found to be small in scale and isolated at the instructor level. This paper presents an exploratory study on institutional approaches to LA in European higher education and discusses prominent challenges that impede LA from reaching its potential. Based on a series of consultations with senior managers from 83 different higher education institutions in 24 European countries, we observe that LA is primarily perceived as a tool to enhance teaching and institutional management. As a result, teaching and support staff are found to be the main users of LA and the target audience of training support. In contrast, there is little evidence of active engagement with students or using LA to develop self-regulated learning skills. We highlight the importance of grounding LA in learning sciences and including students as a key stakeholder in the design and implementation of LA. This paper contributes to our understanding of the development of LA in European higher education and highlights areas to address in both practice and research.

ACS Style

Yi-Shan Tsai; Diego Rates; Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Ioana Jivet; Maren Scheffel; Hendrik Drachsler; Carlos Delgado Kloos; Dragan Gašević. Learning analytics in European higher education—Trends and barriers. Computers & Education 2020, 155, 103933 .

AMA Style

Yi-Shan Tsai, Diego Rates, Pedro Manuel Moreno-Marcos, Pedro J. Muñoz-Merino, Ioana Jivet, Maren Scheffel, Hendrik Drachsler, Carlos Delgado Kloos, Dragan Gašević. Learning analytics in European higher education—Trends and barriers. Computers & Education. 2020; 155 ():103933.

Chicago/Turabian Style

Yi-Shan Tsai; Diego Rates; Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Ioana Jivet; Maren Scheffel; Hendrik Drachsler; Carlos Delgado Kloos; Dragan Gašević. 2020. "Learning analytics in European higher education—Trends and barriers." Computers & Education 155, no. : 103933.

Journal article
Published: 03 March 2020 in Applied Sciences
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In education, several studies have tried to track student persistence (i.e., students’ ability to keep on working on the assigned tasks) using different definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students’ interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students’ profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly affected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students’ average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models.

ACS Style

Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Carlos Alario-Hoyos; Carlos Delgado Kloos. Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning. Applied Sciences 2020, 10, 1722 .

AMA Style

Pedro Manuel Moreno-Marcos, Pedro J. Muñoz-Merino, Carlos Alario-Hoyos, Carlos Delgado Kloos. Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning. Applied Sciences. 2020; 10 (5):1722.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Carlos Alario-Hoyos; Carlos Delgado Kloos. 2020. "Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning." Applied Sciences 10, no. 5: 1722.

Journal article
Published: 01 February 2020 in Computers & Education
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ACS Style

Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Jorge Maldonado-Mahauad; Mar Pérez-Sanagustín; Carlos Alario-Hoyos; Carlos Delgado Kloos. Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Computers & Education 2020, 145, 1 .

AMA Style

Pedro Manuel Moreno-Marcos, Pedro J. Muñoz-Merino, Jorge Maldonado-Mahauad, Mar Pérez-Sanagustín, Carlos Alario-Hoyos, Carlos Delgado Kloos. Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Computers & Education. 2020; 145 ():1.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Jorge Maldonado-Mahauad; Mar Pérez-Sanagustín; Carlos Alario-Hoyos; Carlos Delgado Kloos. 2020. "Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs." Computers & Education 145, no. : 1.

Journal article
Published: 01 January 2020 in IEEE Access
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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.

ACS Style

Pedro Manuel Moreno-Marcos; Ting-Chuen Pong; Pedro J. Munoz-Merino; Carlos Delgado Kloos. Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics. IEEE Access 2020, 8, 5264 -5282.

AMA Style

Pedro Manuel Moreno-Marcos, Ting-Chuen Pong, Pedro J. Munoz-Merino, Carlos Delgado Kloos. Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics. IEEE Access. 2020; 8 (99):5264-5282.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Ting-Chuen Pong; Pedro J. Munoz-Merino; Carlos Delgado Kloos. 2020. "Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics." IEEE Access 8, no. 99: 5264-5282.

Journal article
Published: 10 September 2019 in Sustainability
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To start medical or dentistry studies in Flanders, prospective students need to pass a central admission test. A blended program with four Small Private Online Courses (SPOCs) was designed to support those students. The logs from the platform provide an opportunity to delve into the learners’ interactions and to develop predictive models to forecast success in the test. Moreover, the use of different courses allows analyzing how models can generalize across courses. This article has the following objectives: (1) to develop and analyze predictive models to forecast who will pass the admission test, (2) to discover which variables have more effect on success in different courses, (3) to analyze to what extent models can be generalized to other courses and subsequent cohorts, and (4) to discuss the conditions to achieve generalizability. The results show that the average grade in SPOC exercises using only first attempts is the best predictor and that it is possible to transfer predictive models with enough reliability when some context-related conditions are met. The best performance is achieved when transferring within the same cohort to other SPOCs in a similar context. The performance is still acceptable in a consecutive edition of a course. These findings support the sustainability of predictive models.

ACS Style

Pedro Manuel Moreno-Marcos; Tinne De Laet; Pedro J. Muñoz-Merino; Carolien Van Soom; Tom Broos; Katrien Verbert; Carlos Delgado Kloos. Generalizing Predictive Models of Admission Test Success Based on Online Interactions. Sustainability 2019, 11, 4940 .

AMA Style

Pedro Manuel Moreno-Marcos, Tinne De Laet, Pedro J. Muñoz-Merino, Carolien Van Soom, Tom Broos, Katrien Verbert, Carlos Delgado Kloos. Generalizing Predictive Models of Admission Test Success Based on Online Interactions. Sustainability. 2019; 11 (18):4940.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Tinne De Laet; Pedro J. Muñoz-Merino; Carolien Van Soom; Tom Broos; Katrien Verbert; Carlos Delgado Kloos. 2019. "Generalizing Predictive Models of Admission Test Success Based on Online Interactions." Sustainability 11, no. 18: 4940.

Journal article
Published: 28 November 2018 in IEEE Transactions on Learning Technologies
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ACS Style

Pedro Manuel Moreno-Marcos; Carlos Alario-Hoyos; Pedro J. Munoz-Merino; Iria Estevez-Ayres; Carlos Delgado Kloos. A Learning Analytics Methodology for Understanding Social Interactions in MOOCs. IEEE Transactions on Learning Technologies 2018, 12, 442 -455.

AMA Style

Pedro Manuel Moreno-Marcos, Carlos Alario-Hoyos, Pedro J. Munoz-Merino, Iria Estevez-Ayres, Carlos Delgado Kloos. A Learning Analytics Methodology for Understanding Social Interactions in MOOCs. IEEE Transactions on Learning Technologies. 2018; 12 (4):442-455.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Carlos Alario-Hoyos; Pedro J. Munoz-Merino; Iria Estevez-Ayres; Carlos Delgado Kloos. 2018. "A Learning Analytics Methodology for Understanding Social Interactions in MOOCs." IEEE Transactions on Learning Technologies 12, no. 4: 442-455.

Journal article
Published: 18 November 2018 in Journal of Learning Analytics
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This paper introduces a learning analytics policy and strategy framework developed by a cross-European research project team — SHEILA (Supporting Higher Education to Integrate Learning Analytics), based on interviews with 78 senior managers from 51 European higher education institutions across 16 countries. The framework was developed adapting the RAPID Outcome Mapping Approach (ROMA), which is designed to develop effective strategies and evidence-based policy in complex environments. This paper presents four case studies to illustrate the development process of the SHEILA framework and how it can be used iteratively to inform strategic planning and policy processes in real world environments, particularly for large-scale implementation in higher education contexts. To this end, the selected cases were analyzed at two stages, each a year apart, to investigate the progression of adoption approaches that were followed to solve existing challenges, and identify new challenges that could be addressed by following the SHEILA framework.

ACS Style

Yi-Shane Tsai; Pedro Manuel Moreno-Marcos; Ioana Jivet; Maren Scheffel; Kairit Tammets; Kaire Kollom; Dragan Gašević. The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics. Journal of Learning Analytics 2018, 5, 5–20 -5–20.

AMA Style

Yi-Shane Tsai, Pedro Manuel Moreno-Marcos, Ioana Jivet, Maren Scheffel, Kairit Tammets, Kaire Kollom, Dragan Gašević. The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics. Journal of Learning Analytics. 2018; 5 (3):5–20-5–20.

Chicago/Turabian Style

Yi-Shane Tsai; Pedro Manuel Moreno-Marcos; Ioana Jivet; Maren Scheffel; Kairit Tammets; Kaire Kollom; Dragan Gašević. 2018. "The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics." Journal of Learning Analytics 5, no. 3: 5–20-5–20.

Conference paper
Published: 14 August 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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ACS Style

Jorge Maldonado-Mahauad; Mar Pérez-Sanagustín; Pedro Manuel Moreno-Marcos; Carlos Alario-Hoyos; Pedro J. Muñoz-Merino; Carlos Delgado-Kloos. Predicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 355 -369.

AMA Style

Jorge Maldonado-Mahauad, Mar Pérez-Sanagustín, Pedro Manuel Moreno-Marcos, Carlos Alario-Hoyos, Pedro J. Muñoz-Merino, Carlos Delgado-Kloos. Predicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():355-369.

Chicago/Turabian Style

Jorge Maldonado-Mahauad; Mar Pérez-Sanagustín; Pedro Manuel Moreno-Marcos; Carlos Alario-Hoyos; Pedro J. Muñoz-Merino; Carlos Delgado-Kloos. 2018. "Predicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 355-369.

Review
Published: 17 July 2018 in IEEE Transactions on Learning Technologies
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This paper surveys the state of the art on prediction in MOOCs through a Systematic Literature Review (SLR). The main objectives are: (1) to identify the characteristics of the MOOCs used for prediction, (2) to describe the prediction outcomes, (3) to classify the prediction features, (4) to determine the techniques used to predict the variables, and (5) 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.).

ACS Style

Pedro Manuel Moreno-Marcos; Carlos Alario-Hoyos; Pedro J. Munoz-Merino; Carlos Delgado Kloos. Prediction in MOOCs: A Review and Future Research Directions. IEEE Transactions on Learning Technologies 2018, 12, 384 -401.

AMA Style

Pedro Manuel Moreno-Marcos, Carlos Alario-Hoyos, Pedro J. Munoz-Merino, Carlos Delgado Kloos. Prediction in MOOCs: A Review and Future Research Directions. IEEE Transactions on Learning Technologies. 2018; 12 (3):384-401.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Carlos Alario-Hoyos; Pedro J. Munoz-Merino; Carlos Delgado Kloos. 2018. "Prediction in MOOCs: A Review and Future Research Directions." IEEE Transactions on Learning Technologies 12, no. 3: 384-401.

Articles
Published: 04 April 2018 in Behaviour & Information Technology
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The learning process in a MOOC (Massive Open Online Course) can be improved from knowing in advance learners’ grades on different assignments. This would be very useful to detect problems with enough time to take corrective measures. In this work, the aim is to analyse how different course scores can be predicted, what elements or variables affect the predictions and how much and in which way it is possible to anticipate scores. To do that, data from a MOOC about Java programming have been used. Results show the importance of indicators over the algorithms and that forum-related variables do not add power to predict grades, unlike previous scores. Furthermore, the type of task can vary the results. Regarding the anticipation, it was possible to use data from previous topics but with worse performance, although values were better than those obtained in the first seven days of the current topic.

ACS Style

Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Carlos Alario-Hoyos; Iria Estévez-Ayres; Carlos Delgado Kloos. Analysing the predictive power for anticipating assignment grades in a massive open online course. Behaviour & Information Technology 2018, 37, 1021 -1036.

AMA Style

Pedro Manuel Moreno-Marcos, Pedro J. Muñoz-Merino, Carlos Alario-Hoyos, Iria Estévez-Ayres, Carlos Delgado Kloos. Analysing the predictive power for anticipating assignment grades in a massive open online course. Behaviour & Information Technology. 2018; 37 (10-11):1021-1036.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Pedro J. Muñoz-Merino; Carlos Alario-Hoyos; Iria Estévez-Ayres; Carlos Delgado Kloos. 2018. "Analysing the predictive power for anticipating assignment grades in a massive open online course." Behaviour & Information Technology 37, no. 10-11: 1021-1036.

Conference paper
Published: 01 April 2018 in 2018 IEEE Global Engineering Education Conference (EDUCON)
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Forum messages in MOOCs (Massive Open Online Courses) are the most important source of information about the social interactions happening in these courses. Forum messages can be analyzed to detect patterns and learners' behaviors. Particularly, sentiment analysis (e.g., classification in positive and negative messages) can be used as a first step for identifying complex emotions, such as excitement, frustration or boredom. The aim of this work is to compare different machine learning algorithms for sentiment analysis, using a real case study to check how the results can provide information about learners' emotions or patterns in the MOOC. Both supervised and unsupervised (lexicon-based) algorithms were used for the sentiment analysis. The best approaches found were Random Forest and one lexicon based method, which used dictionaries of words. The analysis of the case study also showed an evolution of the positivity over time with the best moment at the beginning of the course and the worst near the deadlines of peer-review assessments.

ACS Style

Pedro Manuel Moreno-Marcos; Carlos Alario-Hoyos; Pedro J. Munoz-Merino; Iria Estévez-Ayres; Carlos Delgado Kloos. Sentiment analysis in MOOCs: A case study. 2018 IEEE Global Engineering Education Conference (EDUCON) 2018, 1489 -1496.

AMA Style

Pedro Manuel Moreno-Marcos, Carlos Alario-Hoyos, Pedro J. Munoz-Merino, Iria Estévez-Ayres, Carlos Delgado Kloos. Sentiment analysis in MOOCs: A case study. 2018 IEEE Global Engineering Education Conference (EDUCON). 2018; ():1489-1496.

Chicago/Turabian Style

Pedro Manuel Moreno-Marcos; Carlos Alario-Hoyos; Pedro J. Munoz-Merino; Iria Estévez-Ayres; Carlos Delgado Kloos. 2018. "Sentiment analysis in MOOCs: A case study." 2018 IEEE Global Engineering Education Conference (EDUCON) , no. : 1489-1496.

Conference paper
Published: 07 March 2018 in Proceedings of the 8th International Conference on Digital Arts
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ACS Style

Yi-Shan Tsai; Pedro Manuel Moreno-Marcos; Kairit Tammets; Kaire Kollom; Dragan Gasevic. SHEILA policy framework. Proceedings of the 8th International Conference on Digital Arts 2018, 320 -329.

AMA Style

Yi-Shan Tsai, Pedro Manuel Moreno-Marcos, Kairit Tammets, Kaire Kollom, Dragan Gasevic. SHEILA policy framework. Proceedings of the 8th International Conference on Digital Arts. 2018; ():320-329.

Chicago/Turabian Style

Yi-Shan Tsai; Pedro Manuel Moreno-Marcos; Kairit Tammets; Kaire Kollom; Dragan Gasevic. 2018. "SHEILA policy framework." Proceedings of the 8th International Conference on Digital Arts , no. : 320-329.