@article{
 title = {Transforming Learning with Meaningful Technologies},
 type = {article},
 year = {2019},
 identifiers = {[object Object]},
 keywords = {analytics,blended learning,self-regulated learning,time management strategies},
 pages = {555-569},
 volume = {11722},
 websites = {http://link.springer.com/10.1007/978-3-030-29736-7},
 id = {38a8c50c-ba11-31ac-9648-c54ecb6bb394},
 created = {2020-02-05T01:33:05.814Z},
 accessed = {2020-02-04},
 file_attached = {false},
 profile_id = {66be748e-b1e3-36e1-95e1-5830d0ccc3ca},
 group_id = {ed1fa25d-c56b-3067-962d-9d08ff49394c},
 last_modified = {2020-02-05T01:33:06.228Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
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 folder_uuids = {c2955262-615e-4e71-ba11-ca23b3748d20},
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 abstract = {This paper reports the findings of a study that proposed a novel learning analytic methodology that combines process mining with cluster analysis to study time management in the context of blended and online learning. The study was conducted with first-year students (N = 241) who were enrolled in blended learning of a health science course. The study identified four distinct time management tactics and three strategies. The tactics and strategies were interpreted according to the established theoretical framework of self- regulated learning in terms of student decisions about what to study, how long to study, and how to study. The study also identified significant differences in academic performance among students who followed different time manage- ment strategies.},
 bibtype = {article},
 author = {Uzir, Ahmad and Ga, Dragan},
 journal = {Ec-Tel 2019}
}

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