Abstract
The aim of this paper is to survey recent research publications that use Soft Computing methods to answer education-related problems based on the analysis of educational data ‘mined’ mainly from interactive/e-learning systems. Such systems are known to generate and store large volumes of data that can be exploited to assess the learner, the system and the quality of the interaction between them. Educational Data Mining (EDM) and Learning Analytics (LA) are two distinct and yet closely related research areas that focus on this data aiming to address open education-related questions or issues. Besides ‘classic’ data analysis methods such as clustering, classification, identification or regression/analysis of variances, soft computing methods are often employed by EDM and LA researchers to achieve their various tasks. Their very nature as iterative optimization algorithms that avoid the exhaustive search of the solutions space and go for possibly suboptimal solutions yet at realistic time and effort, along with their heavy reliance on rich data sets for training, make soft computing methods ideal tools for the EDM or LA type of problems. Decision trees, random forests, artificial neural networks, fuzzy logic, support vector machines and genetic/evolutionary algorithms are a few examples of soft computing approaches that, given enough data, can successfully deal with uncertainty, qualitatively stated problems and incomplete, imprecise or even contradictory data sets – features that the field of education shares with all humanities/social sciences fields. The present review focuses, therefore, on recent EDM and LA research that employs at least one soft computing method, and aims to identify (i) the major education problems/issues addressed and, consequently, research goals/objectives set, (ii) the learning contexts/settings within which relevant research and educational interventions take place, (iii) the relation between classic and soft computing methods employed to solve specific problems/issues, and (iv) the means of dissemination (publication journals) of the relevant research results. Selection and analysis of a body of 300 journal publications reveals that top research questions in education today seeking answers through soft computing methods refer directly to the issue of quality – a critical issue given the currently dominant educational/pedagogical models that favor e-learning or computer- or technology-mediated learning contexts. Moreover, results identify the most frequently used methods and tools within EDM/LA research and, comparatively, within their soft computing subsets, along with the major journals relevant research is being published worldwide. Weaknesses and issues that need further attention in order to fully exploit the benefits of research results to improve both the learning experience and the learning outcomes are discussed in the conclusions.
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Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49.
Anderson, J. A. (1995). An introduction to neural networks. Boston, MA: MIT Press.
Arnold, K. E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33(1), 10.
Bajaj, V., Sharma, R. (2018). “Smart Education with artificial intelligence based determination of learning styles”. Procedia Computer Science 132, (pp. 834–842), International Conference on Computational Intelligence and Data Science (ICCIDS 2018), Elsevier.
Baker, R. S. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International encyclopedia of education (3rd ed., pp. 112–118). Oxford, UK: Elsevier.
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
Bonissone, P. P. (1997). Soft computing: The convergence of emerging reasoning technologies. Soft Computing, 1(1), 6–18.
Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology & Society, 15(3), 3–26.
Chiappe, A., & Rodriguez, L. P. (2017). Learning analytics in 21st century education: A review. Ensaio, 25(97), 971–991.
Chung, H. M., & Gray, P. (1999). Special section: Data mining. Journal of Management Information Systems, 16(1), 11–17.
Clark, R.C., Mayer, R.E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons.
Cole, J. R., & Persichitte, K. A. (2000). Fuzzy cognitive mapping: Applications in education. International Journal of Intelligent Systems, 15, 1–25.
Cooper, A., Powell, S., Yuan, L., & MacNeill, S. (2013). Survey of the state of analytics in UK HE and FE institutions. CETIS White Paper, S/N, 2013, WP03 available at http://publications.cetis.org.uk/.
Dawson, S., & Siemens, G. (2014). Analytics to literacies: The development of a learning analytics framework for multiliteracies assessment. The International Review of Research in Open and Distance Learning, 15(4), 284–305.
Dawson, S., Gašević, D., Siemens, G., Joksimovic, S. (2014). “Current state and future trends: A citation network analysis of the learning analytics field”, In Proceedings of the 4th Intl. Conf. on Learning Analytics and Knowledge, Indianapolis, USA.
Dewey, J. (1964). The need for a philosophy on education: John Dewey on education. Chicago: University of Chicago Press.
Drigas, A. S., Argyri, K., & Vrettaros, J. (2009). Decade review (1999-2009): Artificial intelligence techniques in student modeling. Communications in Computer and Information Science, 49, 552–556.
Dubois, D., & Prade, H. (1998). Soft computing, fuzzy logic, and artificial intelligence. Soft Computing, 2, 7–11.
Dutt, A., Ismail, M. A., & Herawan, T. A. (2017). Systematic review on educational data mining. IEEE Access, 5(7820050), 15991–16005.
Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K., Bello, R. (2017). “A review on methods and software for fuzzy cognitive maps”, Artificial Intelligence Review, 1–31.
Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317.
Fosnot, C. (1996). “Constructivism: A Psychological theory of learning”, Constructivism: Theory, perspectives, and practice, C. Fosnot, Ed., New York: Teachers College Press, 8–33.
Garrison, D. R., & Vaughan, N. D. (2011). Blended learning in higher education: Framework, principles, and guidelines. Jossey-Bass higher and adult education series: John Wiley & Sons.
Glykas, M. (2010). Fuzzy cognitive maps: Advances in theory, methodologies, tools and applications. Berlin, Heidelberg: Springer Verlag.
Groumpos, P. P. (2016). Deep learning vs. wise learning: A critical and challenging overview. IFAC-PapersOnLine, 49(29), 180–189.
Haykin, S.S. (1999). Neural Network - A Comprehensive Foundation. Upper Saddle River, NJ: Pearson Education.
Kecman, V. (2001). Learning and soft computing. The MIT Press: Bradford Books.
Keegan, D. (1996). Foundations of distance education. London: Routledge.
Kitchenham, B.A. (2004). “Procedures for Undertaking Systematic Reviews”, Joint Technical Report, Computer Science Department, Keele University (TR/SE-0401) and National ICT Australia Ltd. (0400011T.1).
Kosko, B. (1986). Fuzzy cognitive maps. International Journal on Man-Machine Studies, 24, 65–75.
Lockwood, F., Gooley, A. (2001). Innovation in Open & Distance Learning: Successful Development of Online and Web-based Learning. Psychology Press.
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, 950–965.
Maturana, H., & Varela, F. (1987). The tree of knowledge: The biological roots of human understanding (Rev. ed.). Boston: Shambhala.
Mendelsohn, P., Dillenbourg, P. (1994). “Implementing a model of cognitive development in an intelligent learning environment”, Technology-based learning environments: Psychological and educational foundations, (pp. 72–78), S. Vosniadou, E. De Corte and H. Mandl, eds., Berlin: Springer-Verlag.
Merçeron, A. (2015). “Educational Data Mining / Learning Analytics: Methods, Tasks and Current Trends”, In Proceedings of 13th e-Learning Conference of the German Computer Society (DeLFI 2015) & DeLFI Workshop 2015, München, Germany.
Mitra, S., & Acharya, T. (2003). Data mining: Multimedia, soft computing, and bioinformatics. New York: John Wiley.
Nandha Kumar, K. G., & Jayanthila Devi, A. (2017). Perspectives on educational data mining: A study. Man in India, 97(4), 55–60.
Pai, M., McCulloch, M., Colford, J. (2002). “Systematic Review: A Road Map Version 2.2”. Systematic Reviews Group, UC Berkeley, available at https://www.scribd.com/document/294591268/Diagnostic-Systematic-Reviews-Road-Map-V3 .
Papageorgiou, E. I., & Salmeron, J. L. (2013). A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems, 21(1) no. 6208855, 66–79.
Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systemic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.
Papert, S. (1987). Information technology and education: Computer criticism vs. Technocentric thinking. Educational Researcher, 16(1), 22–30.
Papert, S. (1990). Introduction: Constructionist Learning. Idit Harel, ed., Cambridge, MA: MIT media laboratory.
Papert, S. (1993). The Children's machine: Rethinking school in the age of the computer. New York: Basic Books.
Peña-Ayala, A. (2014). “Educational data mining: A survey and a data mining-based analysis of recent works”, Expert Systems with Applications, 41(4 part 1), 1432–1462.
Peña-Ayala, A. (2018). “Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy”, WIREs Data Mining and Knowledge Discovery, 8 (e1243).
Peña-Ayala, A., & Sossa-Azuela, H. (2013). Proactive sequencing based on a causal and fuzzy student model. Smart Innovation, Systems and Technologies, 17, 49–76.
Peña-Ayala, A., Sossa-Azuela, H., & Cervantes-Pérez, F. (2012). Predictive student model supported by fuzzy-causal knowledge and inference. Expert Systems with Applications, 39(5), 4690–4709.
Piaget, J. (1971). Psychology and epistemology: Towards a theory of knowledge. New York: Grossman.
Resnick, L., Collins, A. (1996). “Cognition and learning”, The International Encyclopedia of Educational Technology (pp. 48–54), T. Plomp & D. Ely, eds., 2nd ed., Oxford: Pergamon Press.
Roll, I., & Wylie, R. L. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599.
Roll, I., Russell, D. M., & Gašević, D. (2018). Learning at scale. International Journal of Artificial Intelligence in Education, 28, 471–477.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33, 135–146.
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 40(6) no. 5524021, 601–618.
Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.
Romero, C., Ventura, S., Pechenizky, M., & Baker, R. S. (2010). Handbook of Educational Data Mining. Data mining and knowledge discovery series. Boca Raton, FL: Chapman and Hall/CRC Press.
Ryan, S., Scott, B., Freeman, H., & Patel, D. (2013). The Virtual University: The internet and resource-based learning. Open and Flexible Learning Series: Routledge.
Saridakis, K. M., & Dentsoras, A. J. (2008). Soft computing in engineering design–A review. Advanced Engineering Informatics, 22(2), 202–221.
Scheuer, O., & McLaren, B. M. (2011). Educational data mining. Encyclopedia of the Sciences of Learning: Springer.
Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., & Dillenbourg, P. (2017). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1) no. 7542151, 30–41.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.
Siemens, G., Baker, R.S. (2012). “Learning analytics and educational data mining: Towards communication and collaboration” In: Proc. 2nd International Conference on Learning Analytics and Knowledge, (pp. 252–254), Vancouver, BA, Canada.
Terry, K., Cheney, A. (2016). Utilizing virtual and personal learning environments for optimal learning. IGI Global.
Turkle, S., Papert, S. (1990). Epistemological Pluralism: Styles and Voices Within the Computer Culture: Constructionist Learning. Idit Harel, ed., Cambridge, MA: MIT Media Laboratory.
Upadhya, M. S. (2012). Fuzzy logic based evaluation of performance of students in colleges. Journal of Computer Applications, 1, 6–9.
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110.
Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers in Education, 122, 119–135.
Vygotsky, L. (1978). Mind in society: The development of higher psychological processes. MA: Harvard University Press.
Wallace, R. M. (2003). Online learning in higher education: A review of research on interactions among teachers and students. Education, Communication & Information, 3, 241–280.
Watson, H. J. (2013). All about analytics. International Journal of Business Intelligence Research, 4(1), 1–16.
Winne, P. H., & Baker, R. S. (2013). The potentials of educational data Mining for Researching Metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1), 1–8.
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3), 77–84.
Zadeh, L. A., Klir, G. J., & Yuan, B. (1996). Fuzzy sets, fuzzy logic and fuzzy systems: Selected papers. Danvers, MA: World Scientific Publishing Co..
Reviewed Papers
Abazeed, A., & Khder, M. (2017). A classification and prediction model for student's performance in university level. Journal of Computer Science, 13(7), 228–233.
Abdous, M., & He, W. (2011). Using text mining to uncover students' technology-related problems in live video streaming. British Journal of Educational Technology, 42(1), 40–49.
Agaoglu, M. (2016) Predicting instructor performance using data mining techniques in higher education. IEEE Access, 4, art. no. 7469785, 2379–2387.
Ahadi, A., Hellas, A., Lister, R. (2017). A contingency table derived method for analyzing course data. ACM Transactions on Computing Education, 17 (3), art. no. 13 .
Ahmed, A. I. (2016). Big data for accreditation: A case study of Saudi universities. Journal of Theoretical and Applied Information Technology, 91(1), 130–138.
Akcapinar, G., & Bayazit, A. (2018). Investigating video viewing behaviors of students with different learning approaches using video analytics. Turkish Online Journal of Distance Education, 19(4) art. no. 7, 116–125.
Akçapinar, G., Altun, A., & Askar, P. (2015). Modeling students’ academic performance based on their interactions in an online learning environment. Elementary Education Online, 14(3), 815–824.
Alexandron, G., Ruiperez-Valiente, J. A., Chen, Z., Munoz-Merino, P. J., & Pritchard, D. E. (2017). Copying@scale: Using harvesting accounts for collecting correct answers in a MOOC. Computers in Education, 108, 96–114.
Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers in Education, 58(1), 470–489.
Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers in Education, 62, 130–148.
AlJarrah, A., Thomas, M.K., Shehab, M. (2018). Investigating temporal access in a flipped classroom: procrastination persists. International Journal of Educational Technology in Higher Education,15 (1), no 1.
Almeda, M. V., Zuech, J., Utz, C., Higgins, G., Reynolds, R., & Baker, R. S. (2018). Comparing the factors that predict completion and grades among for-credit and open/mooc students in online learning. Online Learning Journal, 22(1), 1–18.
Almutairi, F. M., Sidiropoulos, N. D., & Karypis, G. (2017). Context-aware recommendation-based learning analytics using tensor and coupled matrix factorization. IEEE Journal on Selected Topics in Signal Processing, 11(5) art. no. 7931546, 729–741.
Anaya, A. R., Luque, M., & Peinado, M. (2016). A visual recommender tool in a collaborative learning experience. Expert Systems with Applications, 45, 248–259.
Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers in Education, 113, 226–242.
Appalla, P., Kuthadi, V. M., & Marwala, T. (2017). An efficient educational data mining approach to support e-learning. Wireless Networks, 23(4), 1011–1024.
Arunachalam, A. S., & Velmurugan, T. (2018). Analyzing student performance using evolutionary artificial neural network algorithm. International Journal of Engineering and Technology (UAE), 7(2.26), 67–73.
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers in Education, 113, 177–194.
Atta-Ur-Rahman, S., Aldhafferi, K., & Alqahtani, N. A. (2018). Educational data mining for enhanced teaching and learning. Journal of Theoretical and Applied Information Technology, 96(14), 4417–4427.
Avila, C., Baldiris, S., Fabregat, R., & Graf, S. (2016). Cocreation and evaluation of inclusive and accessible open educational resources: A mapping toward the IMS caliper. Revista Iberoamericana de Tecnologias del Aprendizaje, 11(3) art. no. 7516709, 167–176.
Bachri, O.S., Kusnadi, Hatta, M., Nurhayati, O.D. (2017). Feature selection based on CHI square in artificial neural network to predict the accuracy of student study period. Intl. Journal of Civil Engineering and Technology, 8 (8), 731–739.
Bagriyanik, S., & Karahoca, A. (2018). Using data mining to identify cosmic function point measurement competence. International Journal of Electrical and Computer Engineering, 8(6), 5253–5259.
Baker, R. S., Clarke-Midura, J., & Ocumpaugh, J. (2016). Towards general models of effective science inquiry in virtual performance assessments. Journal of Computer Assisted Learning, 32(3), 267–280.
Beemer, J., Spoon, K., Fan, J., Stronach, J., Frazee, J. P., Bohonak, A. J., & Levine, R. A. (2018a). Assessing instructional modalities: Individualized treatment effects for personalized learning. Journal of Statistics Education, 26(1), 31–39.
Beemer, J., Spoon, K., He, L., Fan, J., & Levine, R. A. (2018b). Ensemble learning for estimating individualized treatment effects in student success studies. International Journal of Artificial Intelligence in Education, 28(3), 315–335.
Berland, M., Davis, D., & Smith, C. P. (2015). AMOEBA: Designing for collaboration in computer science classrooms through live learning analytics. International Journal of Computer-Supported Collaborative Learning, 10(4), 425–447.
Bezet, A., Duncan, T., & Litvin, K. (2018). Implementation and evaluation of online, synchronous research consultations for graduate students. Library Hi Tech News, 35(6), 4–8.
Bharara, S., Sabitha, S., & Bansal, A. (2018). Application of learning analytics using clustering data Mining for Students’ disposition analysis. Education and Information Technologies, 23(2), 957–984.
Bodily, R., Nyland, R., & Wiley, D. (2017). The RISE framework: Using learning analytics to automatically identify open educational resources for continuous improvement. International Review of Research in Open and Distance Learning, 18(2), 103–122.
Brenner, D. G., Matlen, B. J., Timms, M. J., Gochyyev, P., Grillo-Hill, A., Luttgen, K., & Varfolomeeva, M. (2017). Modeling student learning behavior patterns in an online science inquiry environment. Technology. Knowledge and Learning, 22(3), 405–425.
Brinton, C. G., Chiang, M., Jain, S., Lam, H., Liu, Z., & Wong, F. M. F. (2014). Learning about social learning in MOOCs: From statistical analysis to generative model. IEEE Transactions on Learning Technologies, 7(4) art. no. 6851916, 346–359.
Brooks, C., Erickson, G., Greer, J., & Gutwin, C. (2014). Modelling and quantifying the behaviours of students in lecture capture environments. Computers in Education, 75, 282–292.
Bull, S., & Kay, J. (2016). SMILI: A framework for interfaces to learning data in open learner models, learning analytics and related Fields. International Journal of Artificial Intelligence in Education, 26(1), 293–331.
Burgos, C., Campanario, M. L., Peña, D. D. L., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers and Electrical Engineering, 66, 541–556.
Campagni, R., Merlini, D., Sprugnoli, R., & Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508–5521.
Cano, A. R., Fernández-Manjón, B., & García-Tejedor, Á. J. (2018). Using game learning analytics for validating the design of a learning game for adults with intellectual disabilities. British Journal of Educational Technology, 49(4), 659–672.
Carceller, C., Dawson, S., & Lockyer, L. (2013). Improving academic outcomes: Does participating in online discussion forums payoff? International Journal of Technology Enhanced Learning, 5(2), 117–132.
Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Núñez, J. C. (2016). Students' LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers in Education, 96, 42–54.
Cerezo, R., Esteban, M., Sánchez-Santillán, M, Núñez, J.C. (2017). Procrastinating behavior in computer-based learning environments to predict performance: A case study in Moodle. Frontiers in Psychology, 8 (AUG), art. no. 1403.
Chen, G. (2016). Recommendation method of educational resources under the big data environment. Journal of Computational and Theoretical Nanoscience, 13(4), 2582–2587.
Chen, J., & Zhao, J. (2018). An educational data mining model for supervision of network learning process. International Journal of Emerging Technologies in Learning, 13(11), 67–77.
Chen, Y., Chen, Y., & Oztekin, A. (2017a). A hybrid data envelopment analysis approach to analyse college graduation rate at higher education institutions. INFOR, 55(3), 188–210.
Chen, C.-C., Fu, X., & Chang, C.-Y. (2017b). A terms mining and clustering technique for surveying network and content analysis of academic groups exploration. Cluster Computing, 20(1), 43–52.
Chen, B., Chang, Y.-H., Ouyang, F., & Zhou, W. (2018a). Fostering student engagement in online discussion through social learning analytics. Internet and Higher Education, 37, 21–30.
Chen, G., Davis, D., Krause, M., Aivaloglou, E., Hauff, C., & Houben, G.-J. (2018b). From learners to earners: Enabling MOOC learners to apply their skills and earn money in an online market place. IEEE Transactions on Learning Technologies, 11(2), 264–274.
Chen, P., Lu, Y., Zheng, V. W., Chen, X., & Yang, B. (2018c). KnowEdu: A system to construct knowledge graph for education. IEEE Access, 6, 31553–31563.
Cheng, K.-H., Liang, J.-C., & Tsai, C.-C. (2015). Examining the role of feedback messages in undergraduate students' writing performance during an online peer assessment activity. Internet and Higher Education, 25, 78–84.
Chigne, H. S., Gayo, J. E. L., Obeso, M. E. A., De Pablos, P. O., & Lovelle, J. M. C. (2016). Towards the implementation of the learning analytics in the social learning environments for the technology enhanced assessment in computer engineering education. International Journal of Engineering Education, 32(4), 1637–1646.
Chou, C.-Y., Tseng, S.-F., Chih, W.-C., Chen, Z.-H., Chao, P.-Y., Robert Lai, K., Chan, C.-L., Yu, L.-C., Lin, Y.-L. (2015). Open student models of core competencies at the curriculum level: Using learning analytics for student reflection. IEEE Transactions on Emerging Topics in Computing, 99, no. 7335629.
Cobo, A., Rocha, R., & Rodríguez-Hoyos, C. (2014). Evaluation of the interactivity of students in virtual learning environments using a multicriteria approach and data mining. Behaviour & Information Technology, 33(10), 1000–1012.
Conde, M. A., Garcia-Penalvo, F. J., Gomez-Aguilar, D.-A., & Theron, R. (2015). Exploring software engineering subjects by using visual learning analytics techniques. Revista Iberoamericana de Tecnologias del Aprendizaje, 10(4) art. no. 7293149, 242–252.
Conde, M. A., Colomo-Palacios, R., García-Peñalvo, F. J., & Larrucea, X. (2018). Teamwork assessment in the educational web of data: A learning analytics approach towards ISO 10018. Telematics and Informatics, 35(3), 551–563.
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting student performance from LMS data: A comparison of 17 blended courses using moodle LMS. IEEE Transactions on Learning Technologies, 10(1) art. no. 7589022, 17–29.
Considine, H., Teng, M., Nafalski, A., & Nedic, Z. (2016). Recent developments in remote laboratory NetLab. Global Journal of Engineering Education, 18(1), 16–21.
Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256.
Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M. (2018). The NISPI framework: Analysing collaborative problem-solving from students' physical interactions. Computers in Education, 116, 93–109.
Davies, R., Nyland, R., Bodily, R., Chapman, J., Jones, B., & Young, J. (2017). Designing technology-enabled instruction to utilize learning analytics. TechTrends, 61(2), 155–161.
Dejaeger, K., Goethals, F., Giangreco, A., Mola, L., & Baesens, B. (2012). Gaining insight into student satisfaction using comprehensible data mining techniques. European Journal of Operational Research, 218(2), 548–562.
Del Puerto Paule-Ruiz, M., Riestra-Gonzalez, M., Sanchez-Santillan, M., & Perez-Perez, J. R. (2015). The procrastination related indicators in e-learning platforms. Journal of Universal Computer Science, 21(1), 7–22.
Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506.
Dharmarajan, K., & Dorairangaswamy, M. A. (2017). Discovering student e-learning preferred navigation paths using selection page and time preference algorithm. International Journal of Emerging Technologies in Learning, 12(10), 202–211.
Dimić, G., Predić, B., Rančić, D., Petrović, V., Maček, N., & Spalević, P. (2018). Association analysis of moodle e-tests in blended learning educational environment. Computer Applications in Engineering Education, 26(3), 417–430.
Dragulescu, B., Bucos, M., & Vasiu, R. (2015). Social network analysis on educational data set in RDF format. Journal of Computing and Information Technology, 23(3), 269–281.
Drlík, M., Švec, P., Kapusta, J., Munk, M., Noskova, T., Pavlova, T., Yakovleva, O., Morze, N., & Smyrnova-Trybulska, E. (2017). Identification of differences in university e-environment between selected EU and non-EU countries using knowledge mining methods: Project IRNet case study. International Journal of Web Based Communities, 13(2), 236–261.
Duque, R., Gómez-Pérez, D., Nieto-Reyes, A., & Bravo, C. (2015). Analyzing collaboration and interaction in learning environments to form learner groups. Computers in Human Behavior, 47, 42–49.
Duzhin, F., Gustafsson, A. (2018). Machine learning-based app for self-evaluation of teacher-specific instructional style and tools. Education Sciences, 8 (1), no. 7.
Dzelzkaleja, L. (2018). Color code method design evaluation and data analysis. International Journal of Engineering and Technology (UAE), 7(2), 106–109.
Ebner, M., Edtstadler, K., & Ebner, M. (2018). Tutoring writing spelling skills within a web-based platform for children. Universal Access in the Information Society, 17(2), 305–323.
El Mabrouk, M., Gaou, S., & Rtili, M. K. (2017). Towards an intelligent hybrid recommendation system for E-learning platforms using data mining. International Journal of Emerging Technologies in Learning, 12(6), 52–76.
Elaachak, L., Belahbibe, A., & Bouhorma, M. (2015). Towards a system of guidance, assistance and learning analytics based on multi agent system applied on serious games. International Journal of Electrical and Computer Engineering, 5(2), 344–354.
ElFangary, L. M. (2011). Mining of Egyptian missions data for shaping new paradigms. Education and Information Technologies, 16(2), 139–157.
Elhassan, A., Jenhani, I., & Brahim, G. B. (2018). Remedial actions recommendation via multi-label classification: A course learning improvement method. International Journal of Machine Learning and Computing, 8(6), 583–588.
Estevez-Ayres, I., Arias Fisteus, J., & Delgado-Kloos, C. L. (2017). A distributed stream-based infrastructure for the real-time gathering and analysis of heterogeneous educational data. Journal of Network and Computer Applications, 100, 56–68.
Estévez-Ayres, I., Arias Fisteus, J., Uguina-Gadella, L., Alario-Hoyos, C., & Delgado-Kloos, C. (2018). Uncovering flipped-classroom problems at an engineering course on systems architecture through data-driven learning design. International Journal of Engineering Education, 34(3), 865–878.
Evale, D. S. (2017). Learning management system with prediction model and course-content recommendation module. Journal of Information Technology Education: Research, 16(1), 437–457.
Fidalgo-Blanco, Á., Sein-Echaluce, M. L., García-Peñalvo, F. J., & Conde, M. Á. (2015). Using learning analytics to improve teamwork assessment. Computers in Human Behavior, 47, 149–156.
Fields, D.A., Kafai, Y.B., Giang, M.T. (2017). Youth computational participation in the wild: Understanding experience and equity in participating and programming in the online Scratch community. ACM Transactions on Computing Education, 17 (3), art. no. 15, .
Firat, M. (2016). Determining the effects of lms learning behaviors on academic achievement in a learning analytic perspective. Journal of Information Technology Education: Research, 15(2016), 75–87.
Fong, J., & Wong, K. T. Y. (2015). A personal assistant authoring e-book for e-learning in higher education using inverted files of hyperlinks. International Journal of Innovation and Learning, 18(3), 333–349.
Fratamico, L., Conati, C., Kardan, S., & Roll, I. (2017). Applying a framework for student modeling in exploratory learning environments: Comparing data representation granularity to handle environment complexity. International Journal of Artificial Intelligence in Education, 27(2), 320–352.
Frick, T., & Dagli, C. (2016). MOOCs for research: The case of the Indiana University plagiarism tutorials and tests. Technology, Knowledge and Learning, 21(2), 255–276.
Fulantelli, G., Taibi, D., & Arrigo, M. (2015). A framework to support educational decision making in mobile learning. Computers in Human Behavior, 47, 50–59.
Funk, M., & Van Diggelen, M. (2017). Feedback conversations: Creating feedback dialogues with a new textual tool for industrial design student feedback. International Journal of Web-Based Learning and Teaching Technologies, 12(4), 78–92.
Gašević, D., Mirriahi, N., Dawson, S., & Joksimović, S. (2017). Effects of instructional conditions and experience on the adoption of a learning tool. Computers in Human Behavior, 67, 207–220.
Gómez-Aguilar, D. A., Hernández-García, Á., García-Peñalvo, F. J., & Therón, R. (2015). Tap into visual analysis of customization of grouping of activities in eLearning. Computers in Human Behavior, 47, 60–67.
Gómez-Rey, P., Fernández-Navarro, F., & Barberà, E. (2016). Ordinal regression by a gravitational model in the field of educational data mining. Expert Systems, 33(2), 161–175.
Gowda, S. M., Baker, R. S., Corbett, A. T., & Rossi, L. M. (2013). Towards automatically detecting whether student learning is shallow. International Journal of Artificial Intelligence in Education, 23(1–4), 50–70.
Grünewald, F., & Meinel, C. (2015). Implementation and evaluation of digital E-lecture annotation in learning groups to Foster active learning. IEEE Transactions on Learning Technologies, 8(3) art. no. 7018078, 286–298.
Gursoy, M., Inan, A., Nergiz, M.E., Saygin, Y. (2016). Privacy-Preserving Learning Analytics: Challenges and Techniques. IEEE Transactions on Learning Technologies, 99, art. no. 7563858.
Hadioui, A., El Faddouli, N.-E., Touimi, Y. B., & Mohammed, S. B. (2017). Machine learning based on big data extraction of massive educational knowledge. International Journal of Emerging Technologies in Learning, 12(11), 151–167.
Hanna, N., Richards, D., Jacobson, M.J. (2014). Academic performance in a 3D virtual learning environment: Different learning types vs. different class types. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8863, (p. 15).
Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D. J., & Long, Q. (2018). Predicting academic performance by considering student heterogeneity. Knowledge-Based Systems, 161, 134–146.
Hernández-García, Á., González-González, I., Jiménez-Zarco, A. I., & Chaparro-Peláez, J. (2015). Applying social learning analytics to message boards in online distance learning: A case study. Computers in Human Behavior, 47, 68–80.
Hershkovitz, A., & Nachmias, R. (2011). Online persistence in higher education web-supported courses. Internet and Higher Education, 14(2), 98–106.
Hlosta, M., Zdrahal, Z., & Zendulka, J. (2018). Are we meeting a deadline? Classification goal achievement in time in the presence of imbalanced data. Knowledge-Based Systems, 160, 278–295.
Hoffait, A.-S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1–11.
Holmes, M., Latham, A., Crockett, K., & O'Shea, J. D. (2018). Near real-time comprehension classification with artificial neural networks: Decoding e-learner non-verbal behavior. IEEE Transactions on Learning Technologies, 11(1), 5–12.
Hook, P. A. (2017). Using course-subject co-occurrence (CSCO) to reveal the structure of an academic discipline: A framework to evaluate different inputs of a domain map. Journal of the Association for Information Science and Technology, 68(1), 182–196.
Hossain, Z., Bumbacher, E., Brauneis, A., Diaz, M., Saltarelli, A., Blikstein, P., & Riedel-Kruse, I. H. (2018). Design guidelines and empirical case study for scaling authentic inquiry-based science learning via open online courses and interactive biology cloud labs. International Journal of Artificial Intelligence in Education, 28(4), 478–507.
Howard, S. K., Ma, J., & Yang, J. (2016). Student rules: Exploring patterns of students' computer-efficacy and engagement with digital technologies in learning. Computers in Education, 101, 29–42.
Howard, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. Internet and Higher Education, 37, 66–75.
Hu, Y.-H., Lo, C.-L., & Shih, S.-P. (2014). Developing early warning systems to predict students' online learning performance. Computers in Human Behavior, 36, 469–478.
Huda, M., Maseleno, A., Shahrill, M., Jasmi, K. A., Mustari, I., & Basiron, B. (2017). Exploring adaptive teaching competencies in big data era. International Journal of Emerging Technologies in Learning, 12(3), 68–83.
Hui, Y. K., Mai, B., Qian, S., & Kwok, L. F. (2018). Cultivating better learning attitudes: a preliminary longitudinal study. Open Learning, 33(2), 155–170.
Hung, Y. H., Chang, R. I., & Lin, C. F. (2016). Hybrid learning style identification and developing adaptive problem-solving learning activities. Computers in Human Behavior, 55, 552–561.
Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students’ academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447–459.
Iam-On, N., & Boongoen, T. (2017). Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. International Journal of Machine Learning and Cybernetics, 8(2), 497–510.
Iglesias-Pradas, S., Ruiz-De-Azcárate, C., & Agudo-Peregrina, Á. F. (2015). Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies. Computers in Human Behavior, 47, 81–89.
Intayoad, W., Kamyod, C., & Temdee, P. (2018). Synthetic minority over-sampling for improving imbalanced data in educational web usage mining. ECTI Transactions on Computer and Information Technology, 12(2), 118–129.
Išljamović, S., Jeremić, V., & Lalić, S. (2016). Indicators of study success related to impact of university students’ enrollment status. Croatian Journal of Education, 18(2), 583–606.
Jan, S.K. (2018). Identifying online communities of inquiry in higher education using social network analysis. Research in Learning Technology, 26, art. no. 2064, .
Jena, R. K. (2018). Predicting students’ learning style using learning analytics: a case study of business management students from India. Behaviour & Information Technology, 37(10–11), 978–992.
Ji, H., Park, K., Jo, J., & Lim, H. (2016). Mining students activities from a computer supported collaborative learning system based on peer to peer network. Peer-to-Peer Networking and Applications, 9(3), 465–476.
Jo, I.-H., Kim, D., & Yoon, M. (2015). Constructing proxy variables to measure adult learners' time management strategies in LMS. Educational Technology & Society, 18(3), 214–225.
Joksimović, S., Gašević, D., Loughin, T. M., Kovanović, V., & Hatala, M. (2015). Learning at distance: Effects of interaction traces on academic achievement. Computers in Education, 87art. no. 2870, 204–217.
Ju, S.-H., Song, M.-H., Ryu, G.-A., Kim, M., & Yoo, K.-H. (2014). Design and implementation of a dynamic educational content viewer with big data analytics functionality. International Journal of Multimedia and Ubiquitous Engineering, 9(12), 73–84.
Kahan, T., Soffer, T., & Nachmias, R. (2017). Types of participant behavior in a massive open online course. International Review of Research in Open and Distance Learning, 18(6), 1–18.
Kang, J., Liu, M., & Qu, W. (2016). Using gameplay data to examine learning behavior patterns in a serious game. Computers in Human Behavior, 72, 757–770.
Kausar, S., Huahu, X., Hussain, I., Wenhao, Z., & Zahid, M. (2018). Integration of data mining clustering approach in the personalized E-learning system. IEEE Access, 6art. no. 8540811, 72724–72734.
Kelly, N., Montenegro, M., Gonzalez, C., Clasing, P., Sandoval, A., Jara, M., Saurina, E., & Alarcοn, R. (2017). Combining event- and variable-centred approaches to institution-facing learning analytics at the unit of study level. International Journal of Information and Learning Technology, 34(1), 63–78.
Khan, A., & Ghosh, S. K. (2018). Data mining based analysis to explore the effect of teaching on student performance. Education and Information Technologies, 23(4), 1677–1697.
Khousa, E. A., & Atif, Y. (2018). Social network analysis to influence career development. Journal of Ambient Intelligence and Humanized Computing, 9(3), 601–616.
Kim, D., Park, Y., Yoon, M., & Jo, I.-H. (2016). Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. Internet and Higher Education, 30, 30–43.
Kim, D., Yoon, M., Jo, I.-H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea. Computers in Education, 127, 233–251.
Kinnebrew, J. S., Killingsworth, S. S., Clark, D. B., Biswas, G., Sengupta, P., Minstrell, J., Martinez-Garza, M., & Krinks, K. (2017). Contextual markup and Mining in Digital Games for science learning: Connecting player behaviors to learning goals. IEEE Transactions on Learning Technologies, 10(1) art. no. 7390255, 93–103.
Kotsiantis, S., Tselios, N., Filippidi, A., & Komis, V. (2013). Using learning analytics to identify successful learners in a blended learning course. International Journal of Technology Enhanced Learning, 5(2), 133–150.
Kuhnel, M., Seiler, L., Honal, A., & Ifenthaler, D. (2018). Mobile learning analytics in higher education: Usability testing and evaluation of an app prototype. Interactive Technology and Smart Education, 15(4), 332–347.
Kumaran, S. R., Othman, M. S., & Yusuf, L. M. (2016). Data mining approaches in business intelligence: Postgraduate data analytic. Jurnal Teknologi, 78(8–2), 75–79.
Kuo, M.-S., & Chuang, T.-Y. (2016). How gamification motivates visits and engagement for online academic dissemination - An empirical study. Computers in Human Behavior, 55art. no. 3652, 16–27.
Kuosa, K., Distante, D., Tervakari, A., Cerulo, L., Fernández, A., Koro, J., & Kailanto, M. (2016). Interactive visualization tools to improve learning and teaching in online learning environments. International Journal of Distance Education Technologies, 14(1), 1–21.
Laakso, M.-J., Kaila, E., & Rajala, T. (2018). ViLLE – Collaborative education tool: Designing and utilizing an exercise-based learning environment. Education and Information Technologies, 23(4), 1655–1676.
Lacave, C., Molina, A. I., & Cruz-Lemus, J. A. (2018). Learning analytics to identify dropout factors of computer science studies through Bayesian networks. Behaviour & Information Technology, 37(10–11), 993–1007.
Lagus, J., Longi, K., Klami, A., Hellas, A. (2018). Transfer-learning methods in programming course outcome prediction. ACM Transactions on Computing Education, 18 (4), art. no. 19,
Lara, J. A., Lizcano, D., Martínez, M. A., Pazos, J., & Riera, T. (2014). A system for knowledge discovery in e-learning environments within the European higher education area - application to student data from Open University of Madrid, UDIMA. Computers in Education, 72, 23–26.
Laugerman, M., Rover, D., Shelley, M., & Mickelson, S. (2015). Determining graduation rates in engineering for community college transfer students using data mining. International Journal of Engineering Education, 31(6), 1448–1457.
Lee, Y.-J. (2017). Modeling students’ problem solving performance in the computer-based mathematics learning environment. International Journal of Information and Learning Technology, 34(5), 385–395.
Lee, S., Kim, S.-H., & Kwon, B. C. (2017). VLAT: Development of a visualization literacy assessment test. IEEE Transactions on Visualization and Computer Graphics, 23(1) art. no. 7539634, 551–560.
Lei, Y.-R., Lei, L., & Liu, L.-Q. (2017). Application of fuzzy association rules in the analysis on higher vocational college students' performance. Journal of Computers (Taiwan), 28(1), 1–12.
Leong, C. K., Lee, Y. H., & Mak, W. K. (2012). Mining sentiments in SMS texts for teaching evaluation. Expert Systems with Applications, 39(3), 2584–2589.
Lepp, M., Palts, T., Luik, P., Papli, K., Suviste, R., Säde, M., Hollo, K., Vaherpuu, V., & Tõnisson, E. (2018). Troubleshooters for tasks of introductory programming MOOCs. International Review of Research in Open and Distance Learning, 19(4), 56–75.
Li, Y., Sun, J., & Qiang, W. (2015). Application of data mining in personalized remote distance education web system. Open Cybernetics and Systemics Journal, 9(1), 1769–1775.
Lian, D.-F., & Liu, Q. (2018). Jointly recommending library books and predicting academic performance: A mutual reinforcement perspective. Journal of Computer Science and Technology, 33(4), 654–667.
Lin, C., Liu, D., Pang, W., & Wang, Z. (2015). Sherlock: A semi-automatic framework for quiz generation using a hybrid semantic similarity measure. Cognitive Computation, 7(6), 667–679.
Liu, H., Ma, W., Yang, Y., & Carbonell, J. (2016). Learning concept graphs from online educational data. Journal of Artificial Intelligence Research, 55, 1059–1090.
Liu, S., Ni, C., Liu, Z., Peng, X., & Cheng, H. N. H. (2017). Mining individual learning topics in course reviews based on author topic model. International Journal of Distance Education Technologies, 15(3), 1–14.
Liu, Q., Wu, R., Chen, E., Xu, G., Su, Y., Chen, Z., Hu, G. (2018). Fuzzy cognitive diagnosis for modelling examinee performance. ACM Transactions on Intelligent Systems and Technology, 9 (4), art. no. 48, .
Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90–97.
Luo, C. (2016). Application of data mining in web-based education platform. Revista de la Facultad de Ingenieria, 31(6), 84–93.
Lynch, C. F. (2017). Who prophets from big data in education? New insights and new challenges. Theory and Research in Education, 15(3), 249–271.
Mah, D.-K., & Ifenthaler, D. (2018). Students’ perceptions toward academic competencies: The case of German first-year students. Issues in Educational Research, 28(1), 120–137.
Mandal, L., Das, R., Bhattacharya, S., & Basu, P. N. (2017). Intellimote: A hybrid classifier for classifying learners' emotion in a distributed e-learning environment. Turkish Journal of Electrical Engineering and Computer Sciences, 25(3), 2084–2095.
Manso-Vazquez, M., & Llamas-Nistal, M. (2015). Proposal of a learning organization tool with support for metacognition. Revista Iberoamericana de Tecnologias del Aprendizaje, 10(2) art. no. 7086009, 35–42.
Manso-Vazquez, M., Caeiro-Rodriguez, M., & Llamas-Nistal, M. (2018). An xAPI application profile to monitor self-regulated learning strategies. IEEE Access, 6art. no. 8423185, 42467–42481.
Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers in Education, 103, 1–15.
Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: A case study with high school students. Expert Systems, 33(1), 107–124.
Mat, U. B., & Buniyamin, N. (2017). Using neuro-fuzzy technique to classify and predict electrical engineering students' achievement upon graduation based on mathematics competency. Indonesian Journal of Electrical Engineering and Computer Science, 5(3), 684–690.
Maté, A., De Gregorio, E., Cámara, J., Trujillo, J., & Luján-Mora, S. (2016). The improvement of analytics in massive open online courses by applying data mining techniques. Expert Systems, 33(4), 374–382.
Mayer, R. E. (2008). Learning and instruction. Upper Saddle River, NJ: Pearson Merrill Prentice Hall.
McNaughton, M., Rao, L., & Mansingh, G. (2017). An agile approach for academic analytics: a case study. Journal of Enterprise Information Management, 30(5), 701–722.
Mehmood, R., Alam, F., Albogami, N. N., Katib, I., Albeshri, A., & Altowaijri, S. M. (2017). UTiLearn: A personalised ubiquitous teaching and learning system for smart societies. IEEE Access, 5art. no. 7855752, 2615–2635.
Meier, Y., Xu, J., Atan, O., & Van Der Schaar, M. (2016). Predicting grades. IEEE Transactions on Signal Processing, 64(4) art. no. 7313031, 959–972.
Miguéis, V. L., Freitas, A., Garcia, P. J. V., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36–51.
Minović, M., Milovanović, M., Šošević, U., & Conde González, M. Á. (2015). Visualisation of student learning model in serious games. Computers in Human Behavior, 47, 98–107.
Mittelmeier, J., Long, D., Cin, F. M., Reedy, K., Gunter, A., Raghuram, P., & Rienties, B. (2018). Learning design in diverse institutional and cultural contexts: Suggestions from a participatory workshop with higher education professionals in Africa. Open Learning, 33(3), 250–266.
Mohamad, S. K., Tasir, Z., Harun, J., Shukor, A., & N. (2013). Pattern of reflection in learning authoring system through blogging. Computers in Education, 69, 356–368.
Mohammad Akhriza, T., Ma, Y., & Li, J. (2017). Revealing the gap between skills of students and the evolving skills required by the industry of information and communication technology. International Journal of Software Engineering and Knowledge Engineering, 27(5), 675–698.
Muldner, K., Burleson, W., Van De Sande, B., & Vanlehn, K. (2011). An analysis of students' gaming behaviors in an intelligent tutoring system: Predictors and impacts. User Modeling and User-Adapted Interaction, 21(1–2), 99–135.
Munk, M., Drlik, M., Benko, L., & Reichel, J. (2017). Quantitative and qualitative evaluation of sequence patterns found by application of different educational data preprocessing techniques. IEEE Access, 5art. no. 7932437, 8989–9004.
Muñoz-Merino, P. J., Ruipérez-Valiente, J. A., Alario-Hoyos, C., Pérez-Sanagustín, M., & Delgado Kloos, C. (2015). Precise effectiveness strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs. Computers in Human Behavior, 47, 108–118.
Muñoz-Merino, P. J., Ruipérez-Valiente, J. A., Delgado Kloos, C., Auger, M. A., Briz, S., de Castro, V., & Santalla, S. N. (2017). Lipping the classroom to improve learning with MOOCs technology. Computer Applications in Engineering Education, 25(1), 15–25.
Mwalumbwe, I., & Mtebe, J. S. (2017). Using learning analytics to predict students' performance in moodle learning management system: A case of Mbeya University of science and technology. Electronic Journal of Information Systems in Developing Countries, 79(1) art. no. 1, 1–13.
Nagashree, N., & Pujari, N. V. (2016). A tutor assisting novel electronic framework for qualitative analysis of a question Bank. Computers in Human Behavior, 65, 9–13.
Nam, S., Frishkoff, G., & Collins-Thompson, K. (2018). Predicting Students' disengaged behaviors in an online meaning-generation task. IEEE Transactions on Learning Technologies, 11(3) art. no. 7961199, 362–375.
Natek, S., & Zwilling, M. (2014). Student data mining solution-knowledge management system related to higher education institutions. Expert Systems with Applications, 41(14), 6400–6407.
Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior (Article in Press).
Nistor, N., Baltes, B., Dascǎlu, M., Mihǎilǎ, D., Smeaton, G., & Trǎuşan-Matu, Ş. (2014). Participation in virtual academic communities of practice under the influence of technology acceptance and community factors. A learning analytics application. Computers in Human Behavior, 34, 339–344.
Nkambou, R., Fournier-Viger, P., & Nguifo, E. M. (2011). Learning task models in ill-defined domain using an hybrid knowledge discovery framework. Knowledge-Based Systems, 24(1), 176–185.
Noaman, A. Y., Luna, J. M., Ragab, A. H. M., & Ventura, S. (2016). Recommending degree studies according to students’ attitudes in high school by means of subgroup discovery. International Journal of Computational Intelligence Systems, 9(6), 1101–1117.
Nurhayati, O. D., Bachri, O. S., Supriyanto, A., & Hasbullah, M. (2018). Graduation prediction system using artificial neural network. International Journal of Mechanical Engineering and Technology, 9(7), 1051–1057.
Nyland, R., Davies, R. S., Chapman, J., & Allen, G. (2017). Transaction-level learning analytics in online authentic assessments. Journal of Computing in Higher Education, 29(2), 201–217.
Ognjanovic, I., Gasevic, D., & Dawson, S. (2016). Using non-identifiable data to predict student course selections. Internet and Higher Education, 29, 49–62.
Olanrewaju, R. F., Khan, B. U. I., Mir, R. N., Baba, A. M., & Anwar, F. (2016). Dfam: A distributed feedback analysis mechanism for knowledge based educational big data. Jurnal Teknologi, 78(12–3), 31–38.
Olivares-Rodríguez, C., Guenaga, M., & Garaizar, P. (2018). Using children’s search patterns to predict the quality of their creative problem solving. Aslib Journal of Information Management, 70(5), 538–550.
Osman, G., & Koh, J. H. L. (2013). Understanding management students' reflective practice through blogging. Internet and Higher Education, 16(1), 23–31.
Oztekin, A. (2016). A hybrid data analytic approach to predict college graduation status and its determinative factors. Industrial Management and Data Systems, 116(8), 1678–1699.
Pabreja, K. (2017). Comparison of different classification techniques for educational data. International Journal of Information Systems in the Service Sector, 9(1), 54–67.
Pallavi, S., Lal, K., & Lal, S. P. (2017). Analyse the academic performance of students using ANN classification with modified pillar K-means and IWFA. Wireless Personal Communications, 96(4), 6519–6541.
Palmer, S. (2013). Modelling engineering student academic performance using academic analytics. International Journal of Engineering Education, 29(1), 132–138.
Park, Y., Yu, J. H., & Jo, I.-H. (2016). Clustering blended learning courses by online behavior data case study in a Korean higher education institute. Internet and Higher Education, 29, 1–11.
Peró, M., Soriano, P. P., Capilla, R., Guàrdia, I., Olmos, J., & Hervás, A. (2015). Questionnaire for the assessment of factors related to university degree choice in Spanish public system: A psychometric study. Computers in Human Behavior, 47, 128–138.
Petit, J., Roura, S., Carmona, J., Cortadella, J., Duch, J., Giménez, O., Mani, A., Mas, J., Rodríguez-Carbonell, E., Rubio, E., De San Pedro, E., & Venkataramani, D. (2018). Jutge.Org: Characteristics and experiences. IEEE Transactions on Learning Technologies, 11(3) art. no. 7968379, 321–333.
Phani Krishna, K. V., Mani Kumar, M., & Aruna Sri, P. S. G. (2018). Student information system and performance retrieval through dashboard. International Journal of Engineering and Technology (UAE), 7, 682–685.
Piad, K. C. (2018). Determining the dominant attributes of information technology graduates employability prediction using data mining classification techniques. Journal of Theoretical and Applied Information Technology, 96(12), 3780–3790.
Pintar, D., Begušić, D., Škopljanac-Mačina, F., & Vranić, M. (2018). Automatic extraction of learning concepts from exam query repositories. Journal of Communications Software and Systems, 14(4), 312–319.
Prasad, D., Totaram, R., & Usagawa, T. (2016). Development of open textbooks learning analytics system. International Review of Research in Open and Distance Learning, 17(5), 215–234.
Predić, B., Dimić, G., Rančić, D., Štrbac, P., Maček, N., & Spalević, P. (2018). Improving final grade prediction accuracy in blended learning environment using voting ensembles. Computer Applications in Engineering Education, 26(6), 2294–2306.
Qiang, T. (2016). Data mining algorithm and the effectiveness of mathematics classroom teaching based on support vector machine. International Journal of Database Theory and Application, 9(11), 163–174.
Quinn, D., Albrecht, A., Webby, B., & White, K. (2015). Learning from experience: The realities of developing mathematics courses for an online engineering programme. International Journal of Mathematical Education in Science and Technology, 46(7), 991–1003.
Radosav, D., Brtka, E., & Brtka, V. (2012). Mining association rules from empirical data in the domain of education. International Journal of Computers, Communications and Control, 7(5), 933–944.
Rajendran, R., Iyer, S., Murthy, S., Wilson, C., & Sheard, J. (2013). A theory-driven approach to predict frustration in an ITS (2013). IEEE Transactions on Learning Technologies, 6(4) art. no. 6589592, 378–388.
Ramanathan, L., Geetha, A., Khalid, M., & Swarnalatha, P. (2017). Student performance prediction model based on lion-wolf neural network. International Journal of Intelligent Engineering and Systems, 10(1), 114–123.
Rawson, K., Stahovich, T. F., & Mayer, R. E. (2017). Homework and achievement: Using smartpen technology to find the connection. Journal of Educational Psychology, 109(2), 208–219.
Reamer, A. C., Ivy, J. S., Vila-Parrish, A. R., & Young, R. E. (2015). Understanding the evolution of mathematics performance in primary education and the implications for STEM learning: A Markovian approach. Computers in Human Behavior, 47, 4–17.
Rienties, B., & Toetenel, L. (2016). The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules. Computers in Human Behavior, 60, 333–341.
Rienties, B., Herodotou, C., Olney, T., Schencks, M., & Boroowa, A. (2018a). Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers. International Review of Research in Open and Distance Learning, 19(5), 187–202.
Rienties, B., Lewis, T., McFarlane, R., Nguyen, Q., & Toetenel, L. (2018b). Analytics in online and offline language learning environments: The role of learning design to understand student online engagement. Computer Assisted Language Learning, 31(3), 273–293.
Rodríguez-Cerezo, D., Sarasa-Cabezuelo, A., Gómez-Albarrán, M., & Sierra, J.-L. (2014). Serious games in tertiary education: A case study concerning the comprehension of basic concepts in computer language implementation courses. Computers in Human Behavior, 31(1), 558–570.
Rogaten, J., & Rienties, B. C. (2018). Which first-year students are making most learning gains in stem subjects? Higher Education Pedagogies, 3(1), 161–172.
Román-González, M., Pérez-González, J.-C., Moreno-León, J., & Robles, G. (2018). Can computational talent be detected? Predictive validity of the computational thinking test. International Journal of Child-Computer Interaction, 18, 47–58.
Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers in Education, 68, 458–472.
Ross, S. R. P.-J., Volz, V., Lancaster, M. K., & Divan, A. (2018). A generalizable framework for multi-scale auditing of digital learning provision in higher education. Online Learning Journal, 22(2), 249–270.
Rowe, E., Asbell-Clarke, J., Baker, R. S., Eagle, M., Hicks, A. G., Barnes, T. M., Brown, R. A., & Edwards, T. (2017). Assessing implicit science learning in digital games. Computers in Human Behavior, 76, 617–630.
Rudy, M., & Miranda, E. (2015). Management report for marketing in higher education based on data warehouse and data mining. International Journal of Multimedia and Ubiquitous Engineering, 10(4), 291–302.
Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Leony, D., & Delgado Kloos, C. (2015). ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan academy platform. Computers in Human Behavior, 47, 139–148.
Ruipérez-Valiente, J.A., Muñoz-Merino, P.J., Gascon-Pinedo, J.A., Kloos, C.D. (2017a). Scaling to Massiveness With ANALYSE: A Learning Analytics Tool for Open edX. IEEE Transactions on Human-Machine Systems, PP(99), 1–6.
Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., & Kloos, C. D. (2017b). Detecting and clustering students by their gamification behavior with badges: A case study in engineering education. International Journal of Engineering Education, 33(2), 816–830.
Ruipérez-Valiente, J.A., Muñoz-Merino, P.J., Delgado Kloos, C. (2018). Improving the prediction of learning outcomes in educational platforms including higher level interaction indicators. Expert Systems, 35 (6), art. no. e12298.
Samuel Peter James, I., Ramasubramanian, P., & Magdalene Delighta Angeline, D. (2018). Student learning context analysis by emotional intelligence with data mining tools. International Journal of Intelligent Engineering and Systems, 11(2), 173–183.
San Pedro, M. O. Z., Baker, R. S. J. D., & Rodrigo, M. M. T. (2014). Carelessness and affect in an intelligent tutoring system for mathematics. International Journal of Artificial Intelligence in Education, 24(2), 189–210.
San Pedro, M. O. Z., Baker, R. S., & Heffernan, N. T. (2017). An integrated look at middle school engagement and learning in digital environments as precursors to college attendance. Technology, Knowledge and Learning, 22(3), 243–270.
Sandoval, A., Gonzalez, C., Alarcon, R., Pichara, K., & Montenegro, M. (2018). Centralized student performance prediction in large courses based on low-cost variables in an institutional context. Internet and Higher Education, 37, 76–89.
Santos, O. C., & Boticario, J. G. (2015). User-centred design and educational data mining support during the recommendations elicitation process in social online learning environments. Expert Systems, 32(2), 293–311.
Sarsfield, M., Conway, J. (2018). What can we learn from learning analytics? A case study based on an analysis of student use of video recordings. Research in Learning Technology, 26, art. no. 2087, .
Schmidt, M., Tawfik, A.A. (2017). Using analytics to transform a problem-based case library: An educational design research approach. Interdisciplinary Journal of Problem-based Learning, 12 (1), art. no. 5, .
Schulze, M., & Scholz, K. (2018). Learning trajectories and the role of online courses in a language program. Computer Assisted Language Learning, 31(3), 185–205.
Sedrakyan, G., Snoeck, M., & De Weerdt, J. (2014). Process mining analysis of conceptual modeling behavior of novices - empirical study using JMermaid modeling and experimental logging environment. Computers in Human Behavior, 41, 486–503.
Sedrakyan, G., De Weerdt, J., & Snoeck, M. (2016). Process-mining enabled feedback: "tell me what i did wrong" vs. "tell me how to do it right". Computers in Human Behavior, 57, 352–376.
Şen, B., Uçar, E., & Delen, D. (2012). Predicting and analyzing secondary education placement-test scores: A data mining approach. Expert Systems with Applications, 39(10), 9468–9476.
Senthil Kumar, J., Meganathan, S., Venkataraman, V., & Meena, V. (2017). A data mining approach to classify higher education sector data using Bayesian classifier. International Journal of Mechanical Engineering and Technology, 8(9), 95–103.
Serrano-Laguna, T., Torrente, J., Moreno-Ger, P., & Fernández-Manjón, B. (2014). Application of learning analytics in educational videogames. Entertainment Computing, 5(4), 313–322.
Serrano-Laguna, Á., Manero, B., Freire, M., & Fernández-Manjón, B. (2018). A methodology for assessing the effectiveness of serious games and for inferring player learning outcomes. Multimedia Tools and Applications, 77(2), 2849–2871.
Shapiro, H. B., Lee, C. H.a., Wyman Roth, N. E., Li, K., Çetinkaya-Rundel, M., & Canelas, D. A. (2017). Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers. Computers in Education, 110, 35–50.
Sharada, N., Shashi, M., & Xiong, X. (2018). Modeling student knowledge retention using deep learning and random forests. Journal of Engineering and Applied Sciences, 13(6), 1347–1353.
Shrestha, R. M., Orgun, M. A., & Busch, P. (2016). Offer acceptance prediction of academic placement. Neural Computing and Applications, 27(8), 2351–2368.
Silva, J. C. S., Zambom, E., Rodrigues, R. L., Ramos, J. L. C., & Da Fonseca De Souza, F. (2018). Effects of learning analytics on students' self-regulated learning in flipped classroom. International Journal of Information and Communication Technology Education, 14(3), 91–107.
Singh, A. B., & Mørch, A. I. (2018). An analysis of participants' experiences from the first international MOOC offered at the University of Oslo. Nordic Journal of Digital Literacy, 13(1), 40–64.
Slimani, A., Elouaai, F., Elaachak, L., Yedri, B., & M. (2018). Learning analytics through serious games: Data mining algorithms for performance measurement and improvement purposes. International Journal of Emerging Technologies in Learning, 13(1), 46–64.
Soffer, T., Kahan, T., & Livne, E. (2017). E-assessment of online academic courses via students' activities and perceptions. Studies in Educational Evaluation, 54, 83–93.
Sreenivasa Rao, K., Swapna, N., & Praveen Kumar, P. (2018). Educational data mining for student placement prediction using machine learning algorithms. International Journal of Engineering and Technology (UAE), 7(1.2), 43–46.
Stern, L., Burvill, C., Weir, J., & Field, B. (2016). Metrics to facilitate automated categorization of student learning patterns while using educational engineering software. International Journal of Engineering Education, 32(5), 1888–1902.
Sultana, S., Khan, S., & Abbas, M. A. (2017). Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. International Journal of Electrical Engineering Education, 54(2), 105–118.
Sun, G., Cui, T., Beydoun, G., Chen, S., Dong, F., Xu, D., Shen, J. (2017). Towards massive data and sparse data in adaptive micro open educational resource recommendation: A study on semantic knowledge base construction and cold start problem. Sustainability (Switzerland), 9 (6), art. no. 898, .
Sun, G., Cui, T., Yong, J., Shen, J., & Chen, S. (2018). MLaaS: A cloud-based system for delivering adaptive micro learning in Mobile MOOC learning. IEEE Transactions on Services Computing, 11(2), 292–305.
Sung, C.-Y., Huang, X.-Y., Shen, Y., Cherng, F.-Y., Lin, W.-C., & Wang, H.-C. (2017). Exploring online Learners' interactive dynamics by visually analyzing their time-anchored comments. Computer Graphics Forum, 36(7), 145–155.
Supianto, A. A., Hayashi, Y., & Hirashima, T. (2017). An investigation of learner's actions in posing arithmetic word problem on an interactive learning environment. IEICE Transactions on Information and Systems, E100D(11), 2725–2728.
Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers in Education, 89, 53–74.
Tama, B. A. (2015). Learning to prevent inactive student of Indonesia Open University. Journal of Information Processing Systems, 11(2), 165–172.
Tang, H., Xing, W., & Pei, B. (2018). Exploring the temporal dimension of forum participation in MOOCs. Distance Education, 39(3), 353–372.
Tarus, J. K., Niu, Z., & Yousif, A. (2017). A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72, 37–48.
Teasley, S. D. (2017). Student facing dashboards: One size fits all? Technology, Knowledge and Learning, 22(3), 377–384.
Tekin, A. (2014). Early prediction of students' grade point averages at graduation: A data mining approach. Eurasian Journal of Educational Research, 54, 207–226.
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167.
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2017). Towards actionable learning analytics using dispositions. IEEE Transactions on Learning Technologies, 10(1) art. no. 7839177, 6–16.
Tempelaar, D., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78, 408–420.
Teng, M., Considine, H., Nedic, Z., & Nafalski, A. (2016). Current and future developments in the remote laboratory NetLab. International Journal of Online Engineering, 12(8), 4–12.
Thakar, P., Mehta, A., Manisha (2017). A unified model of clustering and classification to improve students' employability prediction. International Journal of Intelligent Systems and Applications, 9 (9), 10–18.
Thatavarti, S., & Thammi Reddy, K. (2017). Spice: A perspective approach for gap analysis to improve student performance index. International Journal of Applied Engineering Research, 12(19), 8651–8659.
Timmers, C. F., Walraven, A., & Veldkamp, B. P. (2015). The effect of regulation feedback in a computer-based formative assessment on information problem solving. Computers in Education, 87, 1–9.
Tomkin, J.H., West, M., Herman, G.L. (2018). An improved grade point average, with applications to CS undergraduate education analytics. ACM Transactions on Computing Education, 18 (4), art. no. 17.
Tseng, C.-W., Chou, J.-J., & Tsai, Y.-C. (2018). Text mining analysis of teaching evaluation questionnaires for the selection of outstanding teaching faculty members. IEEE Access, 6art. no. 8513821, 72870–72879.
Valle, L.D., Stander, J., Gresty, K., Eales, J., Wei, Y. (2018). Stakeholder perspectives on graphical tools for visualising student assessment and feedback data. Research in Learning Technology, 26, art. no. 1997, .
van den Beemt, A., Buys, J., & van der Aalst, W. (2018). Analysing structured learning behaviour in massive open online courses (MOOCs): An approach based on process mining and clustering. International Review of Research in Open and Distance Learning, 19(5), 38–60.
van der Merwe, A., du Toit, T., & Kruger, H. (2018). A prescriptive specialized learning management system for academic feedback towards improved learning. Journal of Computer Science, 14(10), 1329–1340.
Van Horne, S., Curran, M., Smith, A., VanBuren, J., Zahrieh, D., Larsen, R., & Miller, R. (2018). Facilitating student success in introductory chemistry with feedback in an online platform. Technology, Knowledge and Learning, 23(1), 21–40.
Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2015). Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics. Computers in Education, 90, 80–94.
Varouchas, E., Sicilia, M.-A., & Sánchez-Alonso, S. (2018). Towards an integrated learning analytics framework for quality perceptions in higher education: a 3-tier content, process, engagement model for key performance indicators. Behaviour & Information Technology, 37(10–11), 1129–1141.
Verma, S. K., Thakur, R. S., & Jaloree, S. (2017a). Fuzzy association rule mining based model to predict students' performance. International Journal of Electrical and Computer Engineering, 7(4), 2223–2231.
Verma, P., Sood, S. K., & Kalra, S. (2017b). Smart computing based student performance evaluation framework for engineering education. Computer Applications in Engineering Education, 25(6), 977–991.
Vialardi, C., Chue, J., Peche, J. P., Alvarado, G., Vinatea, B., Estrella, J., & Ortigosa, Á. (2011). A data mining approach to guide students through the enrollment process based on academic performance. User Modeling and User-Adapted Interaction, 21(1–2), 217–248.
Villano, R., Harrison, S., Lynch, G., & Chen, G. (2018). Linking early alert systems and student retention: a survival analysis approach. Higher Education, 76(5), 903–920.
Villaseñor, E. A., Arencibia-Jorge, R., & Carrillo-Calvet, H. (2017). Multiparametric characterization of scientometric performance profiles assisted by neural networks: a study of Mexican higher education institutions. Scientometrics, 110(1), 77–104.
Viswanathan, S. A., & Vanlehn, K. (2018). Using the tablet gestures and speech of pairs of students to classify their collaboration. IEEE Transactions on Learning Technologies, 11(2), 230–242.
Vivian, R., Falkner, K., Falkner, N., Tarmazdi, H. (2016). A method to analyze computer science students' teamwork in online collaborative learning environments. ACM Transactions on Computing Education, 16 (2), art. no. 7.
Volk, H., Kellner, K., & Wohlhart, D. (2015). Learning analytics for english language teaching. Journal of Universal Computer Science, 21(1), 156–174.
Wang, L. (2016). Network teaching system based on a clustering analysis algorithm. World Transactions on Engineering and Technology Education, 14(1), 179–183.
Wang, Y. (2017). Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges. Education Policy Analysis Archives, 25(94).
Wei, X., Lin, H., Yang, L., Yu, Y. (2017). A convolution-LSTM-based deep neural network for cross-domain MOOC forum post classification. Information (Switzerland), 8 (3), art. no. 92.
Wijayanto, F. (2017). Input-support-output model evaluation using clustering analysis on Indonesia high school dataset. Journal of Telecommunication, Electronic and Computer Engineering, 9(2–5), 37–41.
Wook, M., Yusof, Z. M., & Nazri, M. Z. A. (2017). Educational data mining acceptance among undergraduate students. Education and Information Technologies, 22(3), 1195–1216.
Worsley, M., & Blikstein, P. (2018). A multimodal analysis of making. International Journal of Artificial Intelligence in Education, 28(3), 385–419.
Wu, Y., Minkus, T., Ross, K. (2017). Taking the pulse of US college campuses with location-based anonymous mobile apps. ACM Transactions on Intelligent Systems and Technology, 9 (1), art. no. 3078843.
Wu, P., Yu, S., & Wang, D. (2018). Using a learner-topic model for mining learner interests in open learning environments. Educational Technology & Society, 21(2), 192–204.
Wuttke, H.-D., Hamann, M., & Henke, K. (2015). Integration of remote and virtual laboratories in the educational process. International Journal of Online Engineering, 11(3), 62–67.
Xia, Y., Wang, Z., Li, Y., & Hu, J. (2015). Web-based collaborative learning system and its key techniques. International Journal of Multimedia and Ubiquitous Engineering, 10(12), 405–412.
Xie, C., Zhang, Z., Nourian, S., Pallant, A., & Hazzard, E. (2014a). Time series analysis method for assessing engineering design processes using a CAD tool. International Journal of Engineering Education, 30(1), 218–230.
Xie, C., Zhang, Z., Nourian, S., Pallant, A., & Bailey, S. (2014b). On the instructional sensitivity of CAD logs. International Journal of Engineering Education, 30(4), 760–778.
Xie, T., Zheng, Q., Zhang, W., & Qu, H. (2017). Modeling and predicting the active video-viewing time in a large-scale E-learning system. IEEE Access, 5art. no. 7954574, 11490–11504.
Xing, W., & Gao, F. (2018). Exploring the relationship between online discourse and commitment in twitter professional learning communities. Computers in Education, 126, 388–398.
Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable genetic programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181.
Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 58, 119–129.
Yahya, A. A. (2018). Centroid particle swarm optimisation for high-dimensional data classification. Journal of Experimental & Theoretical Artificial Intelligence, 30(6), 857–886.
Yang, S. J. H., Lu, O. H. T., Huang, A. Y.-Q., Huang, J. C.-H., Ogata, H., & Lin, A. J. Q. (2018). Predicting students’ academic performance using multiple linear regression and principal component analysis. Journal of Information Processing, 26, 170–176.
Yu, J., Tao, C., Xu, L., Wu, H., & Liu, F. (2017). Construction of hierarchical cognitive academic map. IEEE Access, 5art. no. 7833131, 2141–2151.
Zaffar, M., Hashmani, M. A., Savita, K. S., & Rizvi, S. S. H. (2018). A study of feature selection algorithms for predicting students academic performance. International Journal of Advanced Computer Science and Applications, 9(5), 541–549.
Zafra, A., & Ventura, S. (2012). Multi-instance genetic programming for predicting student performance in web based educational environments. Applied Soft Computing Journal, 12(8), 2693–2706.
Zhou, X., An, J., Zhao, X., & Dong, Y. (2016). Using data mining on students' learning features: A clustering approach for student classification. Journal of Advanced Computational Intelligence and Intelligent Informatics, 20(7), 1141–1146.
Zhou, Q., Quan, W., Zhong, Y., Xiao, W., Mou, C., & Wang, Y. (2018). Predicting high-risk students using internet access logs. Knowledge and Information Systems, 55(2), 393–413.
Zhuhadar, L., Yang, R., & Lytras, M. D. (2013). The impact of social multimedia systems on cyberlearners. Computers in Human Behavior, 29(2), 378–385.
Zuo, Z., Zhao, K., & Eichmann, D. (2017). The state and evolution of U.S. iSchools: From talent acquisitions to research outcome. Journal of the Association for Information Science and Technology, 68(5), 1266–1277.
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Charitopoulos, A., Rangoussi, M. & Koulouriotis, D. On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: a Review of Years 2010–2018. Int J Artif Intell Educ 30, 371–430 (2020). https://doi.org/10.1007/s40593-020-00200-8
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DOI: https://doi.org/10.1007/s40593-020-00200-8