Blended Learning Evaluation In Higher Education Courses
Although traditional learning was a necessity for centuries and distance learning is sometimes the only way for learning for many learners, the last two decades a supplementary mode to the other modes of learning emerged, the e-learning. However, the last few years, blended learning has dominated as the only mode which combines perfectly the advantages of the other modes of learning.
The role of educational content in blended learning is crucial. The key factor to success is high quality educational content, appropriate for learning and able to fulfill course educational aims and objectives. Most of the times it is not an easy task to give feedback to instructors about the online educational content. However, some course characteristics and students’ actions may reflect the quality and quantity of the educational content.
This study evaluates the use of blended learning in TEI of West Macedonia with the use of structured questionnaires exposed to the learners. The learners express their attitude about how useful the blended learning is and how this blended means facilitates their studies. It proposes two variables Richness and Usefulness, taking into account statistics concerning the courses. These variables aim to help course instructors and administrators review course usage and find course weaknesses.
Keywords: Blended learning, evaluation, questionnaire, richness, usefulness
 C. B. Baruque, M. A. Amaral, A. Barcellos, J. C. Da Silva Freitas, and C. J. Longo, Analysing users’ access logs in Moodle to improve e learning, in Proc. Euro Amer. Conf. Telematics Inf. Syst., Faro, Portugal, pp. 1–4.
 M. Feng and N. Heffernan, Informing teachers live about student learning: Reporting in the assistment system, Technol., Instruction, Cognition, Learn. J, 3, p. 1, (2006).
 M. Feng, J. E. Beck, and N. T. Heffernan, Using learning decomposition and bootstrapping with randomization to compare the impact of different educational interventions on learning, 51–60.
 D. Fichter, Intranet librarian: Intranets and ELearning: A perfect partnership, Online (Wilton, Connecticut), 26, no. 1, p. 68, (2002).
 R. Babo and A. Azevedo, General perspective in learning management systems, Higher Education Institutions and Learning Management Systems: Adoption and Standardization, 1–374, (2011).
 E. García, C. Romero, S. Ventura, and C. D. Castro, An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering, User Modeling and User-Adapted Interaction, 19, no. 1-2, 99– 132, (2009).
 D. R. Garrison and N. Vaughan, Blended learning in higher education, Jossey-Bass, San Francisco, 2007.
 J. A. Garrison, C. Schardt, and J. K. Kochi, Web-based distance continuing education: A new way of thinking for students and instructors, Bulletin of the Medical Library Association, 88, no. 3, 211–217, (2000).
 J. Gibbs and M. Rice, Evaluating student behavior on instructional Web sites using web server logs, p. 1.
 H. L. Grob, F. Bensberg, and F. Kaderali, Controlling open source intermediaries - A web log mining approach, 233–242.
 H. Jin, T. Wu, Z. Liu, and J. Yan, Application of visual data mining in higher-education evaluation system, 101–104.
 N. Hara and R. Kling, Students’ frustrations with a web-based distance education course, First Monday, 4, no. 12, (1999).
 G.-J. Hwang, P.-S. Tsai, C.-C. Tsai, and J. C. R. Tseng, A novel approach for assisting teachers in analyzing student web-searching behaviors, Computers and Education, 51, no. 2, 926–938, (2008).
 A. Ingram, Using web server logs in evaluating instructional web sites, J. Educ. Technol. Syst, 28, no. 2, 137–157, (2003).
 I. Kazanidis, T. Theodosiou, I. Petasakis, and S. Valsamidis, Online courses assessment through measuring and archetyping of usage data, Interactive Leaning Environments, http://www.tandfonline.com/doi/abs/10.1080/10494820.2014.
 I. Kazanidis, S. Valsamidis, S. Kontogiannis, and A. Karakos, B , Courseware Evaluation Through Content, Usage and Marking Assessment, in in Research on e- Learning and ICT in Education Technological, Pedagogical and Instructional Perspectives : Charalampos Karagiannidis, Panagiotis Politis, Ilias Karasavvidis, ISBN, (Online), Eds., 4614–6500, http, //dx.doi.org/10.1007/978-1-4614-6501-0, 2013.
 R. Mazza, Introduction to Information Visualization, Springer-Verlag, New York, 2009.
 D. Monk, Using data mining for e-learning decision making, Electron. J. E-Learning, 3, no. 1, 41–54, (2005).
 P. B. Myszkowski, H. Kwaśnicka, and U. Markowska-Kaczmar, Data mining techniques in e-learning CelGrid system, 315–319
 R. N. Olafsen and D. Cetindamar, E-learning in a competitive firm setting, Innovations in Education and Teaching International, 42, no. 4, 325–335, (2005).
 C. Pahl and C. Donnellan, Data mining technology for the evaluation of web-based teaching and learning systems, in, in Proc. Congr. E-Learning, p. 1, Canada, Montreal, 2003.
 W. W. Porter and C. R. Graham, Institutional drivers and barriers to faculty adoption of blended learning in higher education, British Journal of Educational Technology, 47, no. 4, 748–762, (2016).
 J. Reay, Blended learning - a fusion for the future, Knowledge Management Review, 4, no. 3, p. 6, (2001).
 C. Romero, P. González, S. Ventura, M. J. del Jesus, and F. Herrera, Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data, Expert Systems with Applications, 36, no. 2, 1632–1644, (2009).
 C. Romero, S. Ventura, and P. De Bra, Knowledge discovery with genetic programming for providing feedback to courseware authors, User Modelling and User-Adapted Interaction, 14, no. 5, 425–464, (2004).
 J. E. Rooney, Blending learning opportunities to enhance educational programming and meetings, Association Management, 55, no. 5, 26–32, (2003).
 P. Sands, Inside outside, upside downside: Strategies for connecting online and faceto- face instruction in hybrid courses, in Teaching with Technology Today, 8, upside downside, Strategies for connecting online and face-to-face instruction in hybrid courses. Teaching with Technology Today, 2002.
 A. P. Sanjeev and J. M. Zytkow, Discovering enrolment knowledge in university databases, 246–251
 T. Theodosiou, I. Kazanidis, S. Valsamidis, and S. Kontogiannis, Courseware usage archetyping, 243–249.
 M. Ueno, Data mining and text mining technologies for collaborative learning in an ILMS ”Samurai”, 1052–1053.
 I. Kazanidis, S. Valsamidis, S. Kontogiannis, and A. Karakos, Measuring and mining LMS data, 296–301.
 S. Valsamidis, S. Kontogiannis, I. Kazanidis, and A. Karakos, 2012B, An approach for LMS assessment, 35, 265–283, http://inderscience.metapress.com/content/a26wq71n727973k0/.
 Wang, F. H., 2002, On using data-mining technology for browsing log file analysis in asynchronous learning environment, in Proc. World Conf. Educ. Multimedia, Hypermedia Telecommun., Chesapeake, VA, pp. 2005–2006.
 J. Ward and G. A. La Branche, Blended learning: The convergence of e-learning and meetings. Franchising World, 35, Blended learning, The convergence of e-learning and meetings. Franchising World, 2003.
 A. K. W. Wu and C. H. Leung, Evaluating learning behavior of Web-Based Training (WBT) using Web log, 736–737.
 J. R. Young, Hybrid’ teaching seeks to end the divide between traditional and online instruction, Chronicle of Higher Education, 48, no. 28, A33–A34, (2002).
 C. Zinn and O. Scheuer, Getting to know your student in distance learning contexts, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4227, 437–451, (2006).
 M. E. Zorrilla, E. Menasalvas, D. Marín, E. Mora, and J. Segovia, Web usage mining project for improving Web-based learning sites, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3643, 205–210, (2005).
 L. Zoubek and M. Burda, Visualization of differences in data measuring mathematical skills, 315–324.