[
[1] VIBERG, O. et al.: The current landscape of learning analytics in higher education, Computers in Human Behavior 89, No. 1 (2018) 98–110, doi: 10.1016/j.chb.2018.07.027
]Open DOISearch in Google Scholar
[
[2] BROADBENT, J. – POON, W.: Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review, The Internet and Higher Education 27, No. 1 (2015) 1–15, doi: 0.1002/9781118785317.weom010059
]Open DOISearch in Google Scholar
[
[3] BARNEVELD, A. – ARNOLD, K. – CAMPBELL, J.: Analytics in Higher Education: Establishing a Common Language, Educause Learning Initiative 1, No. 1 (2012) 1–11
]Search in Google Scholar
[
[4] RANJEETH, S. – Latchoumi T. – VICTER, P.: A Survey on Predictive Models of Learning Analytics, Procedia Computer Science 167, No. 1 (2020) 37–46, doi: 10.1016/j.procs.2020.03.180
]Open DOISearch in Google Scholar
[
[5] SCHUMACHER, C. – IFENTHALER, D.: Features students really expect from learning analytics, Computers in Human Behavior 78, No. 1 (2018) 397–407, doi: 10.1016/j.chb.2017.06.030
]Open DOISearch in Google Scholar
[
[6] PRINSLOO, P. – SLADE, S.: An Elephant in the Learning Analytics Room: The Obligation to Act, ACM 7th International Learning Analytics & Knowledge Conference, 2017, pp. 46–55, doi: 10.1145/3027385.3027406
]Open DOISearch in Google Scholar
[
[7] PAPAMITSIOU, Z. – ECONOMIDES, A.: Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence, Educational Technology & Society 17, No. 4 (2014) 49–64
]Search in Google Scholar
[
[8] BIŇAS, M.: Identifying web services for automatic assessments of programming assignments, IEEE 12th International Conference on Emerging eLearning Technologies and Applications (ICETA), 2014, pp. 45–50, doi: 10.1109/ICETA.2014.7107547
]Open DOISearch in Google Scholar
[
[9] ALANAZI, R. et al.: Facilitating program comprehension with call graph multilevel hierarchical abstractions, Journal of Systems and Software 176, No. 1 (2021) 110945:1–110945:15, doi: 10.1016/j.jss.2021.110945
]Open DOISearch in Google Scholar
[
[10] AHSAN, Z. – OBAIDELLAH, U.: Predicting expertise among novice programmers with prior knowledge on programming tasks. IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2020, pp. 1008–1016
]Search in Google Scholar
[
[11] ANDRZEJEWSKA, M. – STOLIŃSKA, A.: Comparing the Difficulty of Tasks Using Eye Tracking Combined with Subjective and Behavioural Criteria, Journal of Eye Movement Research 9, No. 3 (2016) 3:1–3:16, doi: 10.16910/jemr.9.3.3
]Open DOISearch in Google Scholar
[
[12] SULÍR, M. et al.: Large-Scale Dataset of Local Java Software Build Results, Data 5, No. 3 (2020) 1–11, doi: 10.3390/data5030086
]Open DOISearch in Google Scholar
