Massive Open Online Courses: Combining Methodologies and Architecture for a Success Learning

Massive Open Online Courses: Combining Methodologies and Architecture for a Success Learnin

Enjoyed or bored?:A study into achievement emotions and the association with barriers to learning in MOOCs

MOOCs are accessible online personal development opportunities in which learners can expand their knowledge on many topics. Yet, the experience of barriers to learning often hinders learners from achieving their personal learning goals. Therefore, it is important to have insight into determinants that may influence the experience of (certain) barriers. This study investigated whether the emotional determinants enjoyment and boredom, which are known to impact learner achievement and motivation, affect the experience of (specific) barriers while learning in MOOCs. The results show that boredom did affect the experience of barriers related to technical and online related skills, social context and time, support and motivation, yet it did not affect the experience of barriers related to the design of the MOOC. Enjoyment was not correlated to any of the barriers. Furthermore, the same analysis comparing men to women again revealed that boredom did not significantly affect the experience of barriers related to the design of the MOOC, yet did significantly affect the experience of the other barriers. No, significant differences were found between males and females. These findings may serve as input for supporting learners in achieving their individual learning goals

Sentiment analysis in MOOCs: a case study

Proceeding of: 2018 IEEE Global Engineering Education Conference (EDUCON2018), 17-20 April, 2018, Santa Cruz de Tenerife, Canary Islands, Spain.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.This work has been co-funded by the Madrid Regional Government, through the eMadrid Excellence Network (S2013/ICE-2715), by the European Commission through Erasmus+ projects MOOC-Maker (561533-EPP-1-2015-1-ESEPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-1-BEEPPKA3-PI-FORWARD), and LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and by the Spanish Ministry of Economy and Competitiveness, projects SNOLA (TIN2015-71669-REDT), RESET (TIN2014-53199-C3-1-R) and Smartlet (TIN2017-85179-C3-1-R). The latter is financed by the State Research Agency in Spain (AEI) and the European Regional Development Fund (FEDER). It has also been supported by the Spanish Ministry of Education, Culture and Sport, under a FPU fellowship (FPU016/00526).Publicad

Evaluating emotion visualizations using AffectVis, an affect-aware dashboard for students

Purpose: The purpose of this paper is to evaluate four visualizations that represent affective states of students. Design/methodology/approach: An empirical-experimental study approach was used to assess the usability of affective state visualizations in a learning context. The first study was conducted with students who had knowledge of visualization techniques (n=10). The insights from this pilot study were used to improve the interpretability and ease of use of the visualizations. The second study was conducted with the improved visualizations with students who had no or limited knowledge of visualization techniques (n=105). Findings: The results indicate that usability, measured by perceived usefulness and insight, is overall acceptable. However, the findings also suggest that interpretability of some visualizations, in terms of the capability to support emotional awareness, still needs to be improved. The level of students" awareness of their emotions during learning activities based on the visualization interpretation varied depending on previous knowledge of information visualization techniques. Awareness was found to be high for the most frequently experienced emotions and activities that were the most frustrating, but lower for more complex insights such as interpreting differences with peers. Furthermore, simpler visualizations resulted in better outcomes than more complex techniques. Originality/value: Detection of affective states of students and visualizations of these states in computer-based learning environments have been proposed to support student awareness and improve learning. However, the evaluation of visualizations of these affective states with students to support awareness in real life settings is an open issue.The work is partially supported by the eMadrid project (funded by the Regional Government of Madrid) under grant no S2013/ICE-2715, and the RESET project (Ministry of Economy and Competitiveness) under grant RESET TIN2014-53199-C3-1-R. The research is partially financed by the SURF Foundation of the Netherlands and the KU Leuven Research Council (Grant Agreement No C24/16/017, PDM16/044)

Identification of Affective States in MOOCs: A Systematic Literature Review

Massive Open Online Courses (MOOCs) are a type of online coursewere students have little interaction,  no instructor, and in some cases, no deadlines to finisch assignments. For this reason, a better understanding of student affection in MOOCs is importantant could have potential to open new perspectives for this type of course. The recent popularization of tools, code libraries and algorithms for intensive data analysis made possible collect data from text and interaction with the platforms, which can be used to infer correlations between affection and learning. In this context, a bibliographical review was carried out, considering the period between 2012 and 2018, with the goal of identifying which methods are being to identify affective states. Three databases were used: ACM Digital Library, IEEE Xplore and Scopus, and 46 papers were found. The articles revealed that the most common methods are related to data intensive techinques (i.e. machine learning, sentiment analysis and, more broadly, learning analytics). Methods such as physiological signal recognition andself-report were less frequent

O USO DE COMPUTAÇÃO AFETIVA EM MOOCS: UM MAPEAMENTO SISTEMÁTICO

The current growth of technologies reflected in different areas, with regard to education, the growth of virtual teaching modalities and the interest in training is remarkable. In this context, MOOCs bring new teaching and learning methods, but still with a high dropout rate. Studies report that affective states play an important role in learning, affectivity is highlighted here, as, applied to education, it allows the teacher to design the course according to the student's affectivity. Therefore, this paper investigate how the use of affective can help in the creation of MOOCs, verifying its influence on the engagement of students and without the number of graduates. The review was carried out following the methodology of systematic mapping and the results show that the use of affective in MOOCs is an area that is still little addressed in Brazil, and the conduction of researches that investigate this experience allows for an understanding of courses and students.El crecimiento actual de las tecnologías se refleja en diferentes áreas, en lo que respecta a la educación, es destacable el crecimiento de las modalidades de enseñanza virtual y el interés por la formación. En este contexto, los MOOC (Massive Open Online Courses) traen nuevos métodos de enseñanza y aprendizaje, pero aún con una alta tasa de deserción. Los estudios informan que los estados afectivos juegan un papel importante en el aprendizaje. Se pone énfasis en la computación afectiva aplicada a la educación, que permite al docente diseñar el curso de acuerdo a la afectividad del alumno. Por lo tanto, este trabajo busca investigar cómo el uso de la computación afectiva puede ayudar a crear MOOCs, verificando su influencia en el engagement de los estudiantes y el número de egresados. La revisión se llevó a cabo siguiendo la metodología de mapeo sistemático y los resultados muestran que el uso de la computación afectiva en los MOOC es un área aún poco abordada en Brasil, y la conducción de investigaciones que investiguen esta experiencia permite comprender los cursos y estudiantes.O atual crescimento das tecnologias reflete-se em diferentes âmbitos, com relação à educação é notável o crescimento das modalidades de ensino virtuais e o interesse em capacitações. Nesse contexto, os MOOCs (Massive Open Online Courses) trazem novos métodos de ensino e aprendizagem, porém, ainda com um alto índice de evasão. Estudos relatam que os estados afetivos têm papel importante na aprendizagem. Destaca-se aqui a computação afetiva, pois aplicada à educação, possibilita ao professor projetar o curso conforme a afetividade do aluno. Portanto, este trabalho busca investigar como o uso de computação afetiva pode auxiliar na criação de MOOCs, verificando sua influência no engajamento dos estudantes e no número de concluintes. A revisão foi realizada seguindo a metodologia do mapeamento sistemático e os resultados mostram que o uso de computação afetiva em MOOCs é uma área ainda pouco abordada no Brasil, e a realização de pesquisas que investiguem essa experiência possibilita entendimento sobre os cursos e estudantes

Recognizing Multidimensional Engagement of E-learners Based on Multi-channel Data in E-learning Environment

Despite recent advances in MOOC, the current e-learning systems have advantages of alleviating barriers by time differences, and geographically spatial separation between teachers and students. However, there has been a 'lack of supervision' problem that e-learner's learning unit state(LUS) can't be supervised automatically. In this paper, we present a fusion framework considering three channel data sources: 1) videos/images from a camera, 2) eye movement information tracked by a low solution eye tracker and 3) mouse movement. Based on these data modalities, we propose a novel approach of multi-channel data fusion to explore the learning unit state recognition. We also propose a method to build a learning state recognition model to avoid manually labeling image data. The experiments were carried on our designed online learning prototype system, and we choose CART, Random Forest and GBDT regression model to predict e-learner's learning state. The results show that multi-channel data fusion model have a better recognition performance in comparison with single channel model. In addition, a best recognition performance can be reached when image, eye movement and mouse movement features are fused.Comment: 4 pages, 4 figures, 2 table

MOOCs: factors that decrease desertion in students

Estamos viviendo constantes cambios tecnológicos y las necesidades de aprendizaje y capacitación son cada vez mayores. Uno de esos cambios son los cursos masivos abiertos en línea (MOOC). Como este es un nuevo tipo de curso en línea en nuestro entorno y el índice de abandono es alto, es imprescindible su análisis para conocer las razones de este y lograr disminuir la deserción de usuarios en cursos MOOC. Para ello, se propone conocer diferentes factores del problema, como por ejemplo la experiencia previa de los usuarios en la realización de cursos MOOC, el nivel de satisfacción en la interacción con las plataformas que alojan cursos MOOC y la satisfacción en general al finalizar un curso. Además, el conocer el perfil de los usuarios facilitará que los futuros cursos se adapten a ellos, aunque conlleve incrementar el equipo de trabajo. Con el fin de que estos se sientan acompañados, con ánimo de seguir el curso y que posean una nueva experiencia de aprendizaje.We are experiencing constant technological change, which implies that learning and training requirements have been increased, they are increasingly high. One of these changes are Massive Open Online Courses (MOOC). As this is a new technology in our environment, the amount of desertion is high, it is essential to the analysis, to know the reasons of the high amounts of desertion and reduce the dropout of users in MOOC courses. For that, it is proposed to meet the different factors of the problem, for example, previous experience of users in making the MOOC courses, the level of satisfaction in the interaction with platforms that have courses MOOC and satisfaction by the end of a course. Additionally, knowing the user’s profile will make it easier for future courses suit them, although entails increasing the team and tailor a course to users, for the purpose of that they feel supported, intending to follow course and that they possess a new learning experience

Evaluating emotion visualizations using AffectVis, an affect-aware dashboard for students

Purpose - The purpose of this paper is to evaluate four visualizations that represent affective states of students. Design/methodology/approach - An empirical-experimental study approach was used to assess the usability of affective state visualizations in a learning context. The first study was conducted with students who had knowledge of visualization techniques (n=10). The insights from this pilot study were used to improve the interpretability and ease of use of the visualizations. The second study was conducted with the improved visualizations with students who had no or limited knowledge of visualization techniques (n=105). Findings - The results indicate that usability, measured by perceived usefulness and insight, is overall acceptable. However, the findings also suggest that interpretability of some visualizations, in terms of the capability to support emotional awareness, still needs to be improved. The level of students’ awareness of their emotions during learning activities based on the visualization interpretation varied depending on previous knowledge of information visualization techniques. Awareness was found to be high for the most frequently experienced emotions and activities that were the most frustrating, but lower for more complex insights such as interpreting differences with peers. Furthermore, simpler visualizations resulted in better outcomes than more complex techniques. Originality/value - Detection of affective states of students and visualizations of these states in computer-based learning environments have been proposed to support student awareness and improve learning. However, the evaluation of visualizations of these affective states with students to support awareness in real life settings is an open issue