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Table of Contents
Intro; Preface; Organization; Contents; Research Papers; Facilitating Students' Digital Competence: Did They Do It?; Abstract; 1 Introduction; 2 Background; 3 Methodology; 4 Findings; 5 Discussion and Conclusions; Acknowledgement; References; Enjoyed or Bored? A Study into Achievement Emotions and the Association with Barriers to Learning in MOOCs; Abstract; 1 Introduction; 2 Theoretical Background and Related Work; 2.1 Barriers to Learning in MOOCs; 2.2 Achievement Emotions; 3 Method; 3.1 Participants; 3.2 Materials; 3.3 Procedures; 3.4 Data Screening; 4 Results; 5 Discussion; 6 Conclusions
AcknowledgementReferences; Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning; Abstract; 1 Introduction; 2 Identified Factors in the Literature Explaining Thesis Completion and Non-completion; 3 Method; 3.1 Sample and Context; 3.2 Data Collection; 3.3 Data Analysis; 4 Results; 4.1 Predicting Completion and Non-completion; 5 Discussion; References; Analyzing Learners' Behavior Beyond the MOOC: An Exploratory Study; 1 Introduction; 2 Related Work; 3 Exploratory Study; 3.1 Context: Tools and Sample
3.2 Data Categorization and Features3.3 Methods; 4 Results; 4.1 Outside the MOOC Behavioral Patterns; 4.2 General and Weekly Grade Prediction; 5 Conclusions and Future Work; References; Building a Learner Model for a Smartphone-Based Clinical Training Intervention in a Low-Income Context: A Pilot Study; Abstract; 1 Background; 1.1 Additive Factor Models (AFMs) and Performance Factor Models (PFMs); 1.2 The Intervention; 2 Methods; 2.1 Study Design, Setting and Participants; 2.2 Study Variables, and Data Management; 2.3 Statistical Methods, Missing Data, and Sensitivity Analyses; 3 Results
4 Discussion4.1 Summary of Findings; 4.2 Relation to Other Studies; 4.3 Implications of Findings; 4.4 Limitations; 5 Conclusions; Acknowledgements; References; Unsupervised Automatic Detection of Learners' Programming Behavior; 1 Introduction; 2 State of the Art; 3 Unsupervised Automatic Detection of Learners' Programming Behavior; 3.1 Application Dataset; 3.2 Phase 1: Identification of Programming Profiles; 3.3 Phase 2: Identification of Students' Behavioral Trajectories; 3.4 Phase 3: Identification of Significant Behavioral Trajectories; 4 Applying the Process with a Real Dataset
4.1 Phase1: Identification of Programming Profiles4.2 Phase 2: Identification of Students' Behavioral Trajectories; 4.3 Phase 3: Identification of Significant Behavioral Trajectories; 4.4 Discussion of the Results; 5 Conclusion and Future Works; References; ``Mirror, mirror on my search...'': Data-Driven Reflection and Experimentation with Search Behaviour; 1 Introduction; 2 Related Work; 3 A Widget for Reflective Search; 4 Methodology; 4.1 Study 1
Experimental Study; 4.2 Study 2
Field Study; 5 Results; 5.1 RQ1: Users' Reaction to the Widget; 5.2 RQ2: Reflection; 5.3 RQ3: Search Behaviour
AcknowledgementReferences; Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning; Abstract; 1 Introduction; 2 Identified Factors in the Literature Explaining Thesis Completion and Non-completion; 3 Method; 3.1 Sample and Context; 3.2 Data Collection; 3.3 Data Analysis; 4 Results; 4.1 Predicting Completion and Non-completion; 5 Discussion; References; Analyzing Learners' Behavior Beyond the MOOC: An Exploratory Study; 1 Introduction; 2 Related Work; 3 Exploratory Study; 3.1 Context: Tools and Sample
3.2 Data Categorization and Features3.3 Methods; 4 Results; 4.1 Outside the MOOC Behavioral Patterns; 4.2 General and Weekly Grade Prediction; 5 Conclusions and Future Work; References; Building a Learner Model for a Smartphone-Based Clinical Training Intervention in a Low-Income Context: A Pilot Study; Abstract; 1 Background; 1.1 Additive Factor Models (AFMs) and Performance Factor Models (PFMs); 1.2 The Intervention; 2 Methods; 2.1 Study Design, Setting and Participants; 2.2 Study Variables, and Data Management; 2.3 Statistical Methods, Missing Data, and Sensitivity Analyses; 3 Results
4 Discussion4.1 Summary of Findings; 4.2 Relation to Other Studies; 4.3 Implications of Findings; 4.4 Limitations; 5 Conclusions; Acknowledgements; References; Unsupervised Automatic Detection of Learners' Programming Behavior; 1 Introduction; 2 State of the Art; 3 Unsupervised Automatic Detection of Learners' Programming Behavior; 3.1 Application Dataset; 3.2 Phase 1: Identification of Programming Profiles; 3.3 Phase 2: Identification of Students' Behavioral Trajectories; 3.4 Phase 3: Identification of Significant Behavioral Trajectories; 4 Applying the Process with a Real Dataset
4.1 Phase1: Identification of Programming Profiles4.2 Phase 2: Identification of Students' Behavioral Trajectories; 4.3 Phase 3: Identification of Significant Behavioral Trajectories; 4.4 Discussion of the Results; 5 Conclusion and Future Works; References; ``Mirror, mirror on my search...'': Data-Driven Reflection and Experimentation with Search Behaviour; 1 Introduction; 2 Related Work; 3 A Widget for Reflective Search; 4 Methodology; 4.1 Study 1
Experimental Study; 4.2 Study 2
Field Study; 5 Results; 5.1 RQ1: Users' Reaction to the Widget; 5.2 RQ2: Reflection; 5.3 RQ3: Search Behaviour