@incollection{discovery10144816,
       booktitle = {Applying Data Science and Learning Analytics Throughout a Learner's Lifespan},
           title = {Mind the Gap: From Typical LMS Traces to Learning to Learn Journeys},
       publisher = {IGI Global},
            note = {This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.},
         address = {Hershey PA, USA},
          editor = {Goran Trajkovski and Marylee Demete and Heather Hayes},
            year = {2022},
          series = {Advances in Educational Technologies and Instructional Design (AETID)},
          author = {Kent, Carmel and Akanji, Abayomi and du Boulay, Benedict and Bashir, Ibrahim and Fikes, Thomas and Rodr{\'i}guez De Jes{\'u}s, Sue and Ramirez Hall, Alysha and Alvarado, Paul and Jones, Jennifer and Cukurova, Mutlu and Sher, Varshita and Blake, Canan and Fisher, Arthur and Greenwood, Juliet and Luckin, Rose},
             url = {http://doi.org/10.4018/978-1-7998-9644-9.ch001},
        abstract = {Many universities aim to improve students' 'learning to learn' (LTL) skills to prepare them for post-academic life. This requires evaluating LTL and integrating it into the university's curriculum and assessment regimes. Data is essential to provide evidence for the evaluation of LTL, meaning that available data sources must be connected to the types of evidence required for evaluation. This chapter describes a case study using an LTL ontology to connect the theoretical aspects of LTL with a university's existing data sources and to inform the design and application of learning analytics. The results produced by the analytics indicate that LTL can be treated as a dimension in its own right. The LTL dimension has a moderate relationship to academic performance. There is also evidence to suggest that LTL develops at an uneven pace across academic terms and that it exhibits different patterns in online as compared to face-to-face delivery methods.},
        keywords = {Clickstream, Clustering, Data Collection, Higher Education, Hybrid Analysis, Learning
Analytics, Learning Management System, Learning to Learn, Ontology, Process Mining}
}