1 |
Alachiotis, N. S., Stavropoulos, E. C., & Verykios, V. S. (2017). Learning analytics with Excel in a blended learning course. In Proceedings of the 9th International Conference in Open & Distance Learning, Athens, Greece (pp. 8-18).
|
2 |
Augustin, T., Hockemeyer, C., Kickmeier-Rust, M., & Albert, D. (2011). Individualized skill assessment in digital learning games: Basic definitions and mathematical formalism. IEEE Transactions on Learning Technologies, 4(2), 138-148.
DOI
|
3 |
Bentayeb, F. (2008). K-Means based approach for OLAP dimension updates. In Proceedings of the 10th International Conference on Enterprise Information Systems, Barcelona, Spain (pp. 531-534).
|
4 |
Berkman, J. (2017). Automated machine learning won't replace data scientists. Retrieved February 18, 2019 from https://www.datascience.com/blog/automated-machine-learningwont-replace-data-scientists.
|
5 |
Bethea, R. M., Duran, B. S., & Boullion, T. L. (1995). Statistical methods for engineers and scientists (3rd ed.). New York: Marcel Dekker.
|
6 |
Chi, Y., Song, X., Zhou, D., Hino, K. & Tseng. B. L. (2007). Evolutionary spectral clustering by incorporating temporal smoothness. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA (pp. 153-162).
|
7 |
Gkontzis, A. F., Karachristos, C. V., Lazarinis, F., Stavropoulos, E. C., & Verykios, V. S. (2017b). A holistic view on academic wide data through learning analytics dashboards. In Proceedings of the Online, Open and Flexible Higher Education Conference on Opportunities and Impact of New Modes of Teaching, Milton Keynes, UK (pp. 12-27).
|
8 |
Gkontzis, A. F., Karachristos, C. V., Panagiotakopoulos, C. T., Stavropoulos, E. C., & Verykios, V. S. (2017). Sentiment analysis to track emotion and polarity in student fora. In Proceedings of the 21st Pan-Hellenic Conference on Informatic, Larissa, Greece (Article No. 39).
|
9 |
Hosman, C. A., Hansen, B. B., & Holland, P. W. (2010). The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder. Annals of Applied Statistics, 4(2), 849-870.
DOI
|
10 |
Kagklis, V., Karatrantou, A., Tantoula, M., Panagiotakopoulos, C. T., & Verykios, V. S. (2015). A learning analytics methodology for detecting sentiment in student fora: A case study in distance education. European Journal of Open, Distance and E-Learning, 18(2), 74-94.
DOI
|
11 |
Dille, B., & Mezack, M. (1991). Identifying predictors of high risk among community college telecourse students. American Journal of Distance Education, 5(1), 24-35.
DOI
|
12 |
Chovanak, T., Kassak, O., & Bielikova, M. (2017). Behavioral patterns mining for online time personalization. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, Bratislava, Slovakia (pp. 361-362).
|
13 |
Dee, H., Cufi, X., Milani, A., Marian, M., & Poggioni V. (2017). Playfully coding: Embedding computer science outreach in schools. In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education, Bologna, Italy (pp. 176-181).
|
14 |
Dierenfeld, H., & Merceron, A. (2012). Learning analytics with Excel pivot tables. In Proceedings of the 1st Moodle Research Conference, Heraklion, Greece (pp. 115-121).
|
15 |
Dragos, S., Halita, D., & Sacarea, C. (2015). Behavioral pattern mining in web based educational systems. In 2015 23rd International Conference on Software, Telecommunications and Computer Networks, Split, Croatia (pp. 215-219).
|
16 |
ElAtia, S., Ippercie, D., & Zaiane, O.-R. (2016). Data mining and learning analytics: Applications in educational research. Hoboken: John Wiley & Sons.
|
17 |
Firat, M. (2017). How open and distance education students use technology? A large scale study. New Trends and Issues Proceedings on Humanities and Social Sciences, 3(3), 164-171.
|
18 |
Gkontzis, A. F., Karachristos, C. V., Lazarinis, F., Stavropoulos, E. C., & Verykios, V. S. (2017a). Assessing student performance by learning analytics dashboards. In Proceedings of the 9th International Conference in Open & Distance Learning, Athens, Greece (pp. 101-115).
|
19 |
Kamber, M., Han, J., & Chiang, J. Y. (1997). Metarule-guided mining of multi-dimensional association rules using data cubes. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, USA (pp. 207-210).
|
20 |
Kagklis, V., Lionarakis, A., Marketos, G., Panagiotakopoulos, C. T., Stavropoulos, E. C., & Verykios, V. S. (2017). Student admission data analytics for open and distance education in Greece. Journal for Open and Distance Education and Educational Technology, 13(2), 6-16.
|
21 |
Kaucic, B., & Asic, T. (2011). Improving introductory programming with Scratch? In Proceedings of the 34th International Convention, IEEE MIPRO, 2011, Opatija, Croatia (pp. 1095-1100).
|
22 |
Larson, R., & Farber, B. (2012). Elementary statistics: Picturing the world. Boston: Prentice Hall, Pearson.
|
23 |
Lazarinis, F., Karachristos, C. V., Stavropoulos, E. C., & Verykios, V. S. (2018). A blended learning course for playfully teaching basic programming concepts to school teachers. Education and Information Technologies, 23(2), 1237-1249.
|
24 |
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A., Brewer, D., ... van Alstyne, M. (2009). Social science: Computational social science. Science, 323(5915), 721-723.
DOI
|
25 |
Liu, D. Y. Τ., Froissard, J.-C., Richards, D., & Atif, A. (2015). An enhanced learning analytics plugin for Moodle: Student engagement and personalized intervention. In Proceedings of the 32nd Conference of the Australasian Society for Computers in Learning in Tertiary Education, Perth, Australia (pp. 180-189).
|
26 |
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439-1459.
DOI
|
27 |
Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2013). Learning computer science concepts with Scratch. Computer Science Education, 23(3), 239-264.
DOI
|
28 |
Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the American Society for Information Science and Technology, 60(2), 372-380.
DOI
|
29 |
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3), 950-965.
DOI
|
30 |
Mazza, R., & Botturi, L. (2007). Monitoring an online course with the GISMO tool: A case study. Journal of Interactive Learning Research, 18(2), 251-265.
|
31 |
Molenaar, I., & van Campen, C. K. (2016). Learning analytics in practice: The effects of adaptive educational technology Snappet on students' arithmetic skills. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, UK (pp. 538-539).
|
32 |
Parker, A. (1999). A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1(2), 1-10.
|
33 |
Paxinou, E., Sgourou, A., Panagiotakopoulos, C., & Verykios, V. (2017). The item response theory for the assessment of users' performance in a biology virtual laboratory. Journal for Open and Distance Education and Educational Technology, 13(2), 107-123.
|
34 |
Rawassizadeh, R., Momeni, E., Dobbins, C., Gharibshah, J., & Pazzani, M. (2016). Scalable daily human behavioral pattern mining from multivariate temporal data. IEEE Transactions on Knowledge and Data Engineering, 28(11), 3098-3112.
DOI
|
35 |
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
DOI
|
36 |
Romero, C., Ventura, S., Zafra, A., & de Bra, P. (2009). Applying web usage mining for personalizing hyperlinks in webbased adaptive educational systems. Computers & Education, 53(3), 828-840.
DOI
|
37 |
Saez-Lopez, J. M., Roman-Gonzalez, M., & Vazquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two-year case study using 'Scratch' in five schools. Computers & Education, 97, 129-141.
DOI
|
38 |
Sclater, N. (2017). Learning analytics explained. New York: Routledge.
|
39 |
Sin, K., & Muthu, L. (2015). Application of big data in education data mining and learning analytics: A literature review. ICTACT Journal of Soft Computing, 5(4), 1035-1049.
DOI
|
40 |
Steiner, C. M., Kickmeier-Rust, M. D., & Albert, D. (2014). Learning analytics and educational data mining: An overview of recent techniques. In Learning Analytics for and in Serious Games: Proceedings of Joint Workshop of the GALA Network of Excellence and the LEA's BOX Project at EC-TEL 2014, Graz, Austria (pp. 6-15).
|
41 |
Weintrop, D., & Wilensky, U. (2015). To block or not to block, that is the question: Students' perceptions of blocks-based programming. In Proceedings of the 14th International Conference on Interaction Design and Children, Medford, MA, USA (pp. 199-208).
|
42 |
Wise, A. F., Zhao, Y., & Hausknecht, S. N. (2013). Learning analytics for online discussions: A pedagogical model for intervention with embedded and extracted analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge, Leuven, Belgium (pp. 48-56).
|
43 |
SmartKlass. (2017). General plugins (Local): SmartKlass Learning Analytics Moodle. Retrieved January 7, 2019 from https://moodle.org/plugins/local_smart_klass.
|
44 |
Zaiane, O. R., & Luo, J. (2001). Towards evaluating learners' behavior in a web-based distance learning environment. In Proceedings of the IEEE International Conference on Advanced Learning Technologies, Madison, WI, USA (pp. 357-360).
|