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, usefulnes

Analysing Moodle learning behaviour about virtual patients

With the development of Internet, online learning management systems (LMSs) have been used widely for providing teaching platforms. The vast quantities of data that LMSs generate daily are difficult to manage manually. Thus, educational data mining (EDM) is applied to solve this problem. In this thesis, EDM is applied on Moodle log data of a medical course. This course was arranged by problem-based learning (PBL) method, which uses virtual patients (VPs) as a problem, to improve students' diagnostic skills. The aim of this thesis is to analyse Moodle learning behaviour related to the usage of VPs and implement a set of Python algorithms to handle such kind of data. There are two ways are utilised to analyse Moodle log data by EDM: applying data mining techniques and implementing Python scripts. The techniques applied on the first way are attribute weighting and generalized sequential patterns (GSP), while the second way provides Python algorithms about extracting frequencies, sessions, and relationship tables. This thesis shows learning behaviour records and patterns about the usage of each VP. In addition, it gives information about the overall usage of different kinds of activities and resources that Moodle offers. Moreover, Python algorithms implemented in this thesis provide tools to extract frequencies, sessions, and relationship tables of Moodle log data for further research