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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.
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 StyleRaquel 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 StyleRaquel 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.
Collaboration is considered as one of the main drivers of learning and it has been broadly studied across numerous contexts, including Massive Open Online Courses (MOOCs). The research on MOOCs has risen exponentially during the last years and there have been a number of works focused on studying collaboration. However, these previous studies have been restricted to the analysis of collaboration based on the forum and social interactions, without taking into account other possibilities such as the synchronicity in the interactions with the platform. Therefore, in this work we performed a case study with the goal of implementing a data-driven approach to detect and characterize collaboration in MOOCs. We applied an algorithm to detect synchronicity links based on their submission times to quizzes as an indicator of collaboration, and applied it to data from two large Coursera MOOCs. We found three different profiles of user accounts, that were grouped in couples and larger communities exhibiting different types of associations between user accounts. The characterization of these user accounts suggested that some of them might represent genuine online learning collaborative associations, but that in other cases dishonest behaviors such as free-riding or multiple account cheating might be present. These findings call for additional research on the study of the kind of collaborations that can emerge in online settings.
José A. Ruipérez-Valiente; Daniel Jaramillo-Morillo; Srećko Joksimović; Vitomir Kovanović; Pedro J. Muñoz-Merino; Dragan Gašević. Data-driven detection and characterization of communities of accounts collaborating in MOOCs. Future Generation Computer Systems 2021, 125, 590 -603.
AMA StyleJosé A. Ruipérez-Valiente, Daniel Jaramillo-Morillo, Srećko Joksimović, Vitomir Kovanović, Pedro J. Muñoz-Merino, Dragan Gašević. Data-driven detection and characterization of communities of accounts collaborating in MOOCs. Future Generation Computer Systems. 2021; 125 ():590-603.
Chicago/Turabian StyleJosé A. Ruipérez-Valiente; Daniel Jaramillo-Morillo; Srećko Joksimović; Vitomir Kovanović; Pedro J. Muñoz-Merino; Dragan Gašević. 2021. "Data-driven detection and characterization of communities of accounts collaborating in MOOCs." Future Generation Computer Systems 125, no. : 590-603.
Massive open online courses (MOOCs) pose a challenge for instructors when trying to provide personalised support to learners, due to large numbers of registered participants. Conversational agents can be of help to support learners when working with MOOCs. This article presents an adaptive learning module for JavaPAL, a conversational agent that complements a MOOC on Java programming, helping learners review the key concepts of the MOOC. This adaptive learning module adapts the difficulty of the questions provided to learners considering their level of knowledge using item response theory (IRT) and also provides recommendations of video fragments extracted from the MOOC for when learners fail questions. The adaptive learning module for JavaPAL has been evaluated showing good usability and learnability through the system usability scale (SUS), reasonably suitable video fragments recommendations for learners, and useful visualisations generated as part of the IRT-based adaptation of questions for instructors to better understand what is happening in the course, to design exams, and to redesign the course content.
Nuria González-Castro; Pedro J. Muñoz-Merino; Carlos Alario-Hoyos; Carlos Delgado Kloos. Adaptive learning module for a conversational agent to support MOOC learners. Australasian Journal of Educational Technology 2021, 37, 24 -44.
AMA StyleNuria González-Castro, Pedro J. Muñoz-Merino, Carlos Alario-Hoyos, Carlos Delgado Kloos. Adaptive learning module for a conversational agent to support MOOC learners. Australasian Journal of Educational Technology. 2021; 37 (2):24-44.
Chicago/Turabian StyleNuria González-Castro; Pedro J. Muñoz-Merino; Carlos Alario-Hoyos; Carlos Delgado Kloos. 2021. "Adaptive learning module for a conversational agent to support MOOC learners." Australasian Journal of Educational Technology 37, no. 2: 24-44.
The assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT (Internet of Things), the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a more objective and automated assessment of surgical technical skills. This paper presents a systematic literature review of papers published after 2013 discussing the objective and automated assessment of surgical technical skills. 101 out of an initial list of 537 papers were analyzed to identify: 1) the sensors used; 2) the data collected by these sensors and the relationship between these data, surgical technical skills and surgeons’ levels of expertise; 3) the statistical methods and algorithms used to process these data; and 4) the feedback provided based on the outputs of these statistical methods and algorithms. Particularly, 1) mechanical and electromagnetic sensors are widely used for tool tracking, while inertial measurement units are widely used for body tracking; 2) path length, number of sub-movements, smoothness, fixation, saccade and total time are the main indicators obtained from raw data and serve to assess surgical technical skills such as economy, efficiency, hand tremor, or mind control, and distinguish between two or three levels of expertise (novice/intermediate/advanced surgeons); 3) SVM (Support Vector Machines) and Neural Networks are the preferred statistical methods and algorithms for processing the data collected, while new opportunities are opened up to combine various algorithms and use deep learning; and 4) feedback is provided by matching performance indicators and a lexicon of words and visualizations, although there is considerable room for research in the context of feedback and visualizations, taking, for example, ideas from learning analytics.
Pablo Castillo-Segura; Carmen Fernández-Panadero; Carlos Alario-Hoyos; Pedro J. Muñoz-Merino; Carlos Delgado Kloos. Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review. Artificial Intelligence in Medicine 2021, 112, 102007 .
AMA StylePablo Castillo-Segura, Carmen Fernández-Panadero, Carlos Alario-Hoyos, Pedro J. Muñoz-Merino, Carlos Delgado Kloos. Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review. Artificial Intelligence in Medicine. 2021; 112 ():102007.
Chicago/Turabian StylePablo Castillo-Segura; Carmen Fernández-Panadero; Carlos Alario-Hoyos; Pedro J. Muñoz-Merino; Carlos Delgado Kloos. 2021. "Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review." Artificial Intelligence in Medicine 112, no. : 102007.
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.
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 StyleSergio 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 StyleSergio 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.
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.
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 StyleKaire 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 StyleKaire 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.
Tal y como ocurre en otros campos de investigación, el desarrollo de la analítica del aprendizaje está influido por las redes de investigadores que contribuyen al mismo. Este artículo describe una de estas redes: la Red Española de Analítica de Aprendizaje (SNOLA). El artículo presenta las líneas de investigación de los miembros de SNOLA, así como los principales retos que la analítica del aprendizaje tiene que afrontar en los próximos años desde la visión de estos investigadores. Este análisis está basado en datos de archivo de SNOLA y en una encuesta realizada a los actuales miembros de la red. Aunque esta aproximación no cubre toda la actividad relacionada con analítica del aprendizaje en España, los resultados proporcionan una visión general representativa del estado de la investigación relacionada con analítica del aprendizaje en dicho contexto. El artículo muestra cuáles son estas tendencias y los principales retos, entre los que se encuentran la necesidad de adoptar un compromiso ético con los datos, desarrollar sistemas que respondan a las necesidades de los usuarios y alcanzar mayor impacto institucional.
Alejandra Martínez Monés; Yannis Dimitriadis Damoulis; Emiliano Acquila-Natale; Ainhoa Álvarez; Manuel Caeiro Rodríguez; Ruth Cobos Pérez; Miguel Ángel Conde González; Francisco José García Peñalvo; Davinia Hernández Leo; Iratxe Menchaca Sierra; Pedro José Muñoz-Merino; Salvador Ros; Teresa Sancho Vinuesa. Achievements and challenges in learning analytics in Spain: The view of SNOLA. RIED. Revista Iberoamericana de Educación a Distancia 2020, 23, 187 -212.
AMA StyleAlejandra Martínez Monés, Yannis Dimitriadis Damoulis, Emiliano Acquila-Natale, Ainhoa Álvarez, Manuel Caeiro Rodríguez, Ruth Cobos Pérez, Miguel Ángel Conde González, Francisco José García Peñalvo, Davinia Hernández Leo, Iratxe Menchaca Sierra, Pedro José Muñoz-Merino, Salvador Ros, Teresa Sancho Vinuesa. Achievements and challenges in learning analytics in Spain: The view of SNOLA. RIED. Revista Iberoamericana de Educación a Distancia. 2020; 23 (2):187-212.
Chicago/Turabian StyleAlejandra Martínez Monés; Yannis Dimitriadis Damoulis; Emiliano Acquila-Natale; Ainhoa Álvarez; Manuel Caeiro Rodríguez; Ruth Cobos Pérez; Miguel Ángel Conde González; Francisco José García Peñalvo; Davinia Hernández Leo; Iratxe Menchaca Sierra; Pedro José Muñoz-Merino; Salvador Ros; Teresa Sancho Vinuesa. 2020. "Achievements and challenges in learning analytics in Spain: The view of SNOLA." RIED. Revista Iberoamericana de Educación a Distancia 23, no. 2: 187-212.
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.
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 StylePedro 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 StylePedro 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.
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.
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 StyleYi-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 StyleYi-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.
In Latin American universities, Learning Analytics (LA) has been perceived as a promising opportunity to leverage data to meet the needs of a diverse student cohort. Although universities have been collecting educational data for years, the adoption of LA in this region is still limited due to the lack of expertise and policies for processing and using educational data. In order to get a better picture of how existing data‐related practices and policies might affect the incorporation of LA in Latin American institutions, we conducted a mixed methods study in four Latin American universities (two Chilean and two Ecuadorian). In this paper, the qualitative data were based on 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students; the quantitative data were collected through two surveys answered by 1884 students and 368 teachers, respectively. The findings reveal opportunities to incorporate LA services into existing data practices in the four case studies. However, the lack of reliable information systems and policies to regulate the use of data imposes challenges that need to be overcome for future LA adoption.
Isabel Hilliger; Margarita Ortiz-Rojas; Paola Pesántez-Cabrera; Eliana Scheihing; Yi-Shan Tsai; Pedro J. Muñoz-Merino; Tom Broos; Alexander Whitelock-Wainwright; Dragan Gašević; Mar Pérez-Sanagustín. Towards learning analytics adoption: A mixed methods study of data‐related practices and policies in Latin American universities. British Journal of Educational Technology 2020, 51, 915 -937.
AMA StyleIsabel Hilliger, Margarita Ortiz-Rojas, Paola Pesántez-Cabrera, Eliana Scheihing, Yi-Shan Tsai, Pedro J. Muñoz-Merino, Tom Broos, Alexander Whitelock-Wainwright, Dragan Gašević, Mar Pérez-Sanagustín. Towards learning analytics adoption: A mixed methods study of data‐related practices and policies in Latin American universities. British Journal of Educational Technology. 2020; 51 (4):915-937.
Chicago/Turabian StyleIsabel Hilliger; Margarita Ortiz-Rojas; Paola Pesántez-Cabrera; Eliana Scheihing; Yi-Shan Tsai; Pedro J. Muñoz-Merino; Tom Broos; Alexander Whitelock-Wainwright; Dragan Gašević; Mar Pérez-Sanagustín. 2020. "Towards learning analytics adoption: A mixed methods study of data‐related practices and policies in Latin American universities." British Journal of Educational Technology 51, no. 4: 915-937.
Tools are an essential support in any human activity. As the technology advances, we are able to design more advanced tools that help us in doing the activities more efficiently. Recently, we have seen breakthroughs in the two main components of tools, namely the interface and the computing engine behind. Natural interfaces allow us to communicate with tools in a way better adapted for humans. In relation to the engine, we are shifting from a computing paradigm to another one based on artificial intelligence, which learns as it is used. In this paper, we examine how these technological advances have an impact on education, leading to smart learning environments.
Carlos Delgado Kloos; Carlos Alario-Hoyos; Pedro J. Munoz-Merino; Maria Blanca Ibanez; Iria Estevez-Ayres; Carmen Fernandez-Panadero. Educational Technology in the Age of Natural Interfaces and Deep Learning. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 2020, 15, 26 -33.
AMA StyleCarlos Delgado Kloos, Carlos Alario-Hoyos, Pedro J. Munoz-Merino, Maria Blanca Ibanez, Iria Estevez-Ayres, Carmen Fernandez-Panadero. Educational Technology in the Age of Natural Interfaces and Deep Learning. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje. 2020; 15 (1):26-33.
Chicago/Turabian StyleCarlos Delgado Kloos; Carlos Alario-Hoyos; Pedro J. Munoz-Merino; Maria Blanca Ibanez; Iria Estevez-Ayres; Carmen Fernandez-Panadero. 2020. "Educational Technology in the Age of Natural Interfaces and Deep Learning." IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 15, no. 1: 26-33.
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.
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 StylePedro 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 StylePedro 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.
Learning Analytics (LA) is perceived to be a promising strategy to tackle persisting educational challenges in Latin America, such as quality disparities and high dropout rates. However, Latin American universities have fallen behind in LA adoption compared to institutions in other regions. To understand stakeholders' needs for LA services, this study used mixed methods to collect data in four Latin American Universities. Qualitative data was obtained from 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students, whereas quantitative data was obtained from surveys answered by 1884 students and 368 teaching staff. According to the triangulation of both types of evidence, we found that (1) students need quality feedback and timely support, (2) teaching staff need timely alerts and meaningful performance evaluations, and (3) managers need quality information to implement support interventions. Thus, LA offers an opportunity to integrate data-driven decision-making in existing tasks.
Isabel Hilliger; Margarita Ortiz-Rojas; Paola Pesántez-Cabrera; Eliana Scheihing; Yi-Shan Tsai; Pedro J. Muñoz-Merino; Tom Broos; Alexander Whitelock-Wainwright; Mar Pérez-Sanagustín. Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach. The Internet and Higher Education 2020, 45, 100726 .
AMA StyleIsabel Hilliger, Margarita Ortiz-Rojas, Paola Pesántez-Cabrera, Eliana Scheihing, Yi-Shan Tsai, Pedro J. Muñoz-Merino, Tom Broos, Alexander Whitelock-Wainwright, Mar Pérez-Sanagustín. Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach. The Internet and Higher Education. 2020; 45 ():100726.
Chicago/Turabian StyleIsabel Hilliger; Margarita Ortiz-Rojas; Paola Pesántez-Cabrera; Eliana Scheihing; Yi-Shan Tsai; Pedro J. Muñoz-Merino; Tom Broos; Alexander Whitelock-Wainwright; Mar Pérez-Sanagustín. 2020. "Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach." The Internet and Higher Education 45, no. : 100726.
To assist higher education institutions in meeting the challenge of limited student engagement in the implementation of Learning Analytics services, the Questionnaire for Student Expectations of Learning Analytics (SELAQ) was developed. This instrument contains 12 items, which are explained by a purported two‐factor structure of “Ethical and Privacy Expectations” and “Service Feature Expectations.” As it stands, however, the SELAQ has only been validated with students from UK university, which is problematic on account of the interest in Learning Analytics extending beyond this context. Thus, the aim of the current work was to assess whether the translated SELAQ can be validated in three contexts (an Estonian, a Spanish, and a Dutch University). The findings show that the model provided acceptable fits in both the Spanish and Dutch samples, but was not supported in the Estonian student sample. In addition, an assessment of local fit is undertaken for each sample, which provides important points that need to be considered in future work. Finally, a general comparison of expectations across contexts is undertaken, which are discussed in relation to the General Data Protection Regulation (2018).
Alexander Whitelock‐Wainwright; Dragan Gašević; Yi‐Shan Tsai; Hendrik Drachsler; Maren Scheffel; Pedro J. Muñoz‐Merino; Kairit Tammets; Carlos Delgado Kloos. Assessing the validity of a learning analytics expectation instrument: A multinational study. Journal of Computer Assisted Learning 2020, 36, 209 -240.
AMA StyleAlexander Whitelock‐Wainwright, Dragan Gašević, Yi‐Shan Tsai, Hendrik Drachsler, Maren Scheffel, Pedro J. Muñoz‐Merino, Kairit Tammets, Carlos Delgado Kloos. Assessing the validity of a learning analytics expectation instrument: A multinational study. Journal of Computer Assisted Learning. 2020; 36 (2):209-240.
Chicago/Turabian StyleAlexander Whitelock‐Wainwright; Dragan Gašević; Yi‐Shan Tsai; Hendrik Drachsler; Maren Scheffel; Pedro J. Muñoz‐Merino; Kairit Tammets; Carlos Delgado Kloos. 2020. "Assessing the validity of a learning analytics expectation instrument: A multinational study." Journal of Computer Assisted Learning 36, no. 2: 209-240.
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.
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 StylePedro 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 StylePedro 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.
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.
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 StylePedro 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 StylePedro 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.
Aarón Rubio‐Fernández; Pedro J. Muñoz‐Merino; Carlos Delgado Kloos. A learning analytics tool for the support of the flipped classroom. Computer Applications in Engineering Education 2019, 27, 1168 -1185.
AMA StyleAarón Rubio‐Fernández, Pedro J. Muñoz‐Merino, Carlos Delgado Kloos. A learning analytics tool for the support of the flipped classroom. Computer Applications in Engineering Education. 2019; 27 (5):1168-1185.
Chicago/Turabian StyleAarón Rubio‐Fernández; Pedro J. Muñoz‐Merino; Carlos Delgado Kloos. 2019. "A learning analytics tool for the support of the flipped classroom." Computer Applications in Engineering Education 27, no. 5: 1168-1185.
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 StylePedro 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 StylePedro 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.
Carlos Delgado Kloos; Yannis Dimitriadis; Davinia Hernández-Leo; Pedro J. Muñoz-Merino; Miguel L. Bote-Lorenzo; Mar Carrió; Carlos Alario-Hoyos; Eduardo Gómez-Sánchez; Patricia Santos. SmartLET. Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality 2018, 648 -653.
AMA StyleCarlos Delgado Kloos, Yannis Dimitriadis, Davinia Hernández-Leo, Pedro J. Muñoz-Merino, Miguel L. Bote-Lorenzo, Mar Carrió, Carlos Alario-Hoyos, Eduardo Gómez-Sánchez, Patricia Santos. SmartLET. Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality. 2018; ():648-653.
Chicago/Turabian StyleCarlos Delgado Kloos; Yannis Dimitriadis; Davinia Hernández-Leo; Pedro J. Muñoz-Merino; Miguel L. Bote-Lorenzo; Mar Carrió; Carlos Alario-Hoyos; Eduardo Gómez-Sánchez; Patricia Santos. 2018. "SmartLET." Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality , no. : 648-653.
The advance in technology in the last decade has allowed for the design of multiple innovative educational experiences. Cloud computing, technologies for interaction in a web browser, and free video distribution allowed for the deployment of MOOC platforms. edX, Coursera, or Udacity are educational initiatives that harnessed these technological advances, which were fundamentally computer-based (although mobile apps were developed later with partial functionality).
Carlos Delgado Kloos; Cristina Catalan; Pedro J. Munoz-Merino; Carlos Alario-Hoyos. Design of a Conversational Agent as an Educational Tool. 2018 Learning With MOOCS (LWMOOCS) 2018, 27 -30.
AMA StyleCarlos Delgado Kloos, Cristina Catalan, Pedro J. Munoz-Merino, Carlos Alario-Hoyos. Design of a Conversational Agent as an Educational Tool. 2018 Learning With MOOCS (LWMOOCS). 2018; ():27-30.
Chicago/Turabian StyleCarlos Delgado Kloos; Cristina Catalan; Pedro J. Munoz-Merino; Carlos Alario-Hoyos. 2018. "Design of a Conversational Agent as an Educational Tool." 2018 Learning With MOOCS (LWMOOCS) , no. : 27-30.