Cognitive-Behavioral Therapy

Cognitive-behavioral therapy (CBT) is the merging of behavioral and cognitive therapies that mostly focuses on working with the client in the present. Although there are many approaches to CBT, there tend to be some common features. For example, CBT is generally a directive approach to psychotherapy that helps clients to challenge their problematic thoughts and to change the behaviors associated with those thoughts. In addition, most approaches to CBT are structured and time limited and include some type of homework where the client can practice the cognitive and behavioral strategies learned in the therapeutic setting. This entry focuses mostly on CBT as defined by Aaron Beck, one of the early founders of this approach

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

Humanising Text-to-Speech Through Emotional Expression in Online Courses

This paper outlines an innovative approach to evaluate the emotional content of three online courses using the affective computing approach of prosody detection on two different text-to-speech (TTS) voices in conjunction with human raters judging the emotional content of the text. This work intends to establish the potential variation on the emotional delivery of online educational resources through the use of synthetic voice, which automatically articulates text into audio. Preliminary results from this pilot research suggest that about one out of every three sentences (35%) in a MOOC contained emotional text and two existing assistive technology voices had poor emotional alignment when reading this text. Synthetic voices were more likely to be overly negative when considering their expression as compared to the emotional content of the text they are reading, which was most frequently neutral. We also analyzed a synthetic voice for which we configured the emotional expression to align with course text, which showed promising improvements

Developing self-efficacy through a massive open online course on study skills

Self-efficacy is a strong predictor of academic performance, and an area of interest for higher education institutions. This paper reports on a massive open online course (MOOC) on study skills, aimed at increasing self-efficacy. Participants (n=32) were from Mexico and Colombia, with ages ranging from 21 to 45 years. At the beginning and the end of the MOOC, learners answered a survey that included the General Self-Efficacy Scale, items on specific study skills, and space for optional comments. Findings show statistically significant increases in general self-efficacy after completing the MOOC, as well as in the perceived self-efficacy related to five out of six study skills. Comments suggest that participants are aware of and value their own improvement. For students, MOOCs can represent low-risk, formative opportunities to widen their knowledge and increase their self-efficacy. For academic institutions, well-designed MOOCs on study skills provide a means to support students

Qualitative learning analytics to detect students’ emotional topography on EduOpen

What emotions can students experience in digitally mediated learning processes? In this paper, we connect Learning Analytics to the Grounded Theory in order to analyse the emotional world of students of 11 courses within the EduOpen (www.eduopen.org) massive open online course (MOOC) platform. Namely, we have used NVivo 11 Plus software and have adopted a bottom–up process to analyse the forum dedicated to students’ selfpresentation from all the courses. Proceeding with the analysis, we defined a set of categories composed of a three-level system. At a more general level, we have two dimensions that we named, respectively, ‘Sentiments about shell’ and ‘Sentiments towards the pulp’. Each of these dimensions is composed of a number of ‘child’ categories and subcategories (which are the nodes in NVivo’s language). After defining the entire set of categories and categorising all the texts (which was a circular process), we run some graphs on NVivo showing the hierarchical structure of the dimensions, the relations between the dimensions and the sources and the clusters of dimensions by coding similarity. The results show how some courses are composed of more negative or more positive sentiments (towards the topic and/or the logistic arrangement of the course) and how the motivation dimension characterises the broad emotional dimension of students heavily. In an evidence-based action-research perspective, these results provide interesting suggestions to personalise the learning activities proposed to students by EduOpen

Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments

This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. This paper introduces i) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement, ii) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines and iii) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset’s suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/ MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly

WASABI: a Two Million Song Database Project with Audio and Cultural Metadata plus WebAudio enhanced Client Applications

This paper presents the WASABI project, started in 2017, which aims at (1) the construction of a 2 million song knowledge base that combines metadata collected from music databases on the Web, metadata resulting from the analysis of song lyrics, and metadata resulting from the audio analysis, and (2) the development of semantic applications with high added value to exploit this semantic database. A preliminary version of the WASABI database is already online1 and will be enriched all along the project. The main originality of this project is the collaboration between the algorithms that will extract semantic metadata from the web and from song lyrics with the algorithms that will work on the audio. The following WebAudio enhanced applications will be associated with each song in the database: an online mixing table, guitar amp simulations with a virtual pedal-board, audio analysis visualization tools, annotation tools, a similarity search tool that works by uploading audio extracts or playing some melody using a MIDI device are planned as companions for the WASABI database