@inProceedings{
 title = {Opportunities and challenges in using learning analytics in learning design},
 type = {inProceedings},
 year = {2017},
 identifiers = {[object Object]},
 keywords = {Feedback,Learning analytics,Learning dashboards,Learning design,Meta-cognitive competences,Reflection},
 pages = {209-223},
 volume = {10474 LNCS},
 publisher = {Springer Verlag},
 id = {18d4062c-3977-3a9d-8de8-5e7f15c649d8},
 created = {2020-02-05T00:49:41.405Z},
 accessed = {2020-02-04},
 file_attached = {false},
 profile_id = {66be748e-b1e3-36e1-95e1-5830d0ccc3ca},
 group_id = {ed1fa25d-c56b-3067-962d-9d08ff49394c},
 last_modified = {2020-02-05T00:49:42.850Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {c2955262-615e-4e71-ba11-ca23b3748d20},
 private_publication = {false},
 abstract = {Educational institutions are designing, creating and evaluating courses to optimize learning outcomes for highly diverse student populations. Yet, most of the delivery is still monitored retrospectively with summative evaluation forms. Therefore, improvements to the course design are only implemented at the very end of a course, thus missing to benefit the current cohort. Teachers find it difficult to interpret and plan interventions just-in-time. In this context, Learning Analytics (LA) data streams gathered from ‘authentic’ student learning activities, may provide new opportunities to receive valuable information on the students’ learning behaviors and could be utilized to adjust the learning design already “on the fly” during runtime. We presume that Learning Analytics applied within Learning Design (LD) and presented in a learning dashboard provide opportunities that can lead to more personalized learning experiences, if implemented thoughtfully. In this paper, we describe opportunities and challenges for using LA in LD. We identify three key opportunities for using LA in LD: (O1) using on demand indicators for evidence based decisions on learning design; (O2) intervening during the run-time of a course; and, (O3) increasing student learning outcomes and satisfaction. In order to benefit from these opportunities, several challenges have to be overcome. Following a thorough literature review, we mapped the identified opportunities and challenges in a conceptual model that considers the interaction of LA in LD.},
 bibtype = {inProceedings},
 author = {Schmitz, Marcel and van Limbeek, Evelien and Greller, Wolfgang and Sloep, Peter and Drachsler, Hendrik},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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