Competence analytics

Abstract

A smart learning environment is characterized by the key provision of personalized learning experiences. To approach different degrees of personalization in online learning, this article introduces a framework called Smart Competence Analytics in LEarning (SCALE) that tracks finer-level learning experiences and translates them into opportunities for customized feedback, reflection, and regulation. The SCALE framework is implemented in four layers: the sensing layer, the analysis layer, the competence layer, and the visualization layer. The sensing layer provides the datasets to support context-awareness through state-of-the-art sensing technologies. The analysis layer, by the means of powerful code analysis tools, derives performance metrics (e.g., learner coding metrics) which serve as input to the competence layer to identify proficiency levels of learners. Finally, a learning analytics dashboard called MI-DASH (visualization layer) allows interaction with performance and competence metrics. The current SCALE system has been used in a study to track the habits, performances, and competences of novice programmers. Growth of coding competences of first-year engineering students has been captured in a continuous manner. Students have been provided with customized feedback to optimize their learning paths. The article describes the analytics-based approach pursued in the study and highlights key findings.

Evolution Is not enough: Revolutionizing Current Learning Environments to Smart Learning Environments

“…These information nuggets need to be presented using advanced visualization techniques and smart technologies to enable natural interaction through rich interfaces, and help investigate and analyze learners' behavior, activities, and performance in ubiquitous (field) environments (Mottus et al 2015). Advanced data mining techniques need to be designed to identify relevant patterns, such as where and when learners have difficulties and where their strengths lie (Kumar et al 2014). Teachers in such environments are able to intervene in the learning process and provide learners with advice and support, such as explaining the topic/tasks, pointing to particular learning material, providing learners with the activities that could help them understand the particular topic/task, and creating teams with complementary strengths.…”

Where is the Learning in Learning Analytics? A Systematic Literature Review on the Operationalization of Learning-Related Constructs in the Evaluation of Learning Analytics Interventions

“…A learner can demonstrate the acquisition of knowledge and skills as a product of their learning process. Most key studies in this category operationalize affected learning through grades or test scores or scores [29], [33]- [35], [37], [49], [50], [53], [54], [57], [61], [63], [67]- [70], [74]- [76], which is a direct assessment of learning as performance on a task (e.g., an exam or final test) [94]. Although grades may seem to be a direct operational definition, this is debatable.…”

Automatic Mapping of Student 3D Profiles in Software Metrics for Temporal Analysis of Programming Learning and Scoring Rubrics

“…• Effort measurement and coding complexity [4] • Prediction of performance [7,8] • Programming style analysis [5,9] • Evaluation by software metrics [3,10] • Recognition of signs of plagiarism [3] • Recognition of rubrics [11] • Recommendation of activities [1] • Programming proficiency analysis [12] • Analysis of learning difficulties and good programming practices [3].…”

“…Among the technological solutions of programming learning analysis based on static analysis already proposed, we highlight: the metrics of Halstead and McCabe [4,5,13], the evaluation of programming skills by software metrics [10], the recommendation system of activities according to learning difficulties [1], the analysis of difficulties by software metrics [3], the evaluation of how programming students learn from the analysis of their programming codes [14] and the programming proficiency analysis of SCALE system [12].…”

“…In more recent research on programming learning analysis, in addition to having a concern to better understand the students' learning profiles, there have been attempts to remedy learning difficulties [3]. Other trends in programming learning analysis are proficiency assessment [12], prediction of performances [15] and the classification of profiles by learning levels [2].…”