Customizing Case-Based Learning (CBL) Sessions with Limited Resources and Analysis of their Perceived Effectiveness by the Conducting Facilitators

Introduction: Intense resource involvement invites educationists to think of innovative methods for the continuation of CBL sessions while remaining within the restricted budget. The current study was planned to develop CBL sessions for second-year MBBS class during the endocrine module utilizing minimal resources and to determine the effectiveness of customized CBL methods by qualitatively analyzing experiences of the involved faculty. Methods: This study was conducted at Foundation University, Islamabad in six months after ethical approval. In the first phase, resource limitations like time slots, well-equipped rooms, and trained faculty were identified. CBL method was adapted and modified from the Maastricht PBL ‘Seven Jump’ process. 7 CBL sessions were conducted as per the devised method. Semi-structured interviews of 9 CBL facilitators were recorded, transcribed, validated, and analyzed in the second phase. Results: All facilitators believed that these sessions provided a productive, focused, intense yet enjoyable learning experience. 4 considered that large groups hindered adequate student participation, while 2 out of 9 themselves felt nervous due to large class sizes. Recap by the senior faculty member was suggested. Conclusion: Modified CBL sessions were perceived by facilitators as an enjoyable and intense learning opportunity for both students and themselves, despite being conducted in a large group utilizing minimal resources

Virtualios, papildytos ir mišrios realybės mokymosi sistemų personalizavimas

The paper is aimed to analyse the problem of personalisation of Virtual Reality/Augmented Reality/Mixed Reality (VR/AR/MR) based learning systems. Research results are two-fold: first, the results of systematic literature review are presented, and, second, VR/AR/MR-based learning systems personalisation framework is proposed. First of all, systematic literature review on research topic was conducted in Thomson Reuters Web of Science database and applying Semantic Scholarsearch tool. The review revealed that strides are being made in education using VR/AR/MR, although much needs to be done. The possibilities of VR/AR/MR application in education seem to be endless and bring many advantages to students of all ages. Few are creating content that may be used for educational purposes, with most advances being made in the entertainment industry, but many understand and realise the future and importance of education applying VR/AR/MR. Manystudies argue that new VR/AR/MR-based learning systems are more effective in comparison with traditional ones. Teachers and students like learning content and activities provided by VR/AR/MR technologies. On the other hand, although the concept of VR/AR/MR has already been proposed more than 20 years ago, most applications are still limited to simple visualisation of virtual objects onto spatially limited scenes, and the developed systems did not pass the barrier of  demonstration prototypes. Many authors agree that personalisation of VR/AR/MR-based learning platforms should be further analysed. Original personalisation framework of VR/AR/MR-based learning systems is also presented in the paper. According to the framework, personalisation of VR/AR/MR learning systems should be based on applying learners models and intelligent technologies e.g. expert evaluation, ontologies, recommender systems, software agents etc. This pedagogically sound personalisation framework is aimed to improve learning quality and effectiveness.Darbo paskirtis yra išanalizuoti virtualios realybės / papildytos realybės / mišrios realybės (VR / PR / MR) grindžiamos mokymosi sistemomis personalizavimo problemą. Tyrimo rezultatai yra dvejopi: pirma, pateikiami sisteminės literatūros apžvalgos rezultatai ir antra, siūloma VR / PR / MR technologijomis grindžiama personalizuota sistema. Visų pirma, sisteminė literatūros apžvalga buvo atlikta Thomson Reuters Web of Science duomenų bazėje, naudojant Semantic Scholar paieškos įrankį. Sisteminė literatūros apžvalga parodė, kad nebloga pradžia yra padaryta šioje srityje, nors dar daug ką reikia nuveikti. VR / PR / MR taikymas švietime yra neribotas, todėl tai suteikia daug naudos mokiniams įvairiuose amžiaus grupėse. Nedaug yra atliktų tyrimų, kurie galėtų būti naudojami švietimo tikslams, daugumos autorių tyrimai daromi pramogų versle, tačiau daugelis supranta, VR / PR / MR svarbą švietime. Daugelis tyrimų tvirtina, kad naujosiomis VR/ PR / MR technologijomis grindžiamos sistemos yra efektyvesnės, palyginti su tradiciniais mokymo būdais. Kita vertus, nors VR / PR / MR koncepcija jau buvo pasiūlyta daugiau nei prieš 20 metų, dauguma programų vis dar yra tik paprastos virtualių objektų vizualizacijos ant erdviškai ribotos scenos. Daugelis autorių sutinka, kad toliau turėtų būti analizuojama, kaip personalizuoti VR / PR / MR technologijomis pagrįstas mokymosi platformas. Siūloma VR / PR / MR personalizavimo struktūra taip pat pristatoma šiame darbe. Pagal siūlomą struktūrą, VR / PR / MR mokymosi sistemų personalizavimas turėtų būti grindžiamas mokinių modeliais ir išmaniosiomis technologijomis, pvz.: taikant ekspertų vertinimą, ontologijas, rekomendacines sistemas, programinės įrangos agentus ir t. t. Pedagoginiu požiūriu, personalizavus sistemas siekiama pagerinti mokymosi kokybę ir efektyvumą

New MCEQLS fuzzy AHP methodology for evaluating learning repositories: a tool for technological development of economy

The paper aims to present a new methodology to evaluate the quality of features and functionality of learning object repositories (LORs). The quality of features and functionality of LORs is analysed in terms of engaging LOR users and content producers. Thus, it can be referred to as quality-in-use of LORs. This methodology consists of creation and consequent application of methods and the model for the quality-in-use of LORs. The model of the quality-in-use of LORs is presented in this paper. The methodology for evaluating the quality-in-use of LORs is based on the general MCEQLS (Multiple Criteria Evaluation of the Quality of Learning Software) approach to evaluate the quality of learning software. The essential part of the novel methodology is the application of improved Fuzzy AHP method to establish criteria weights of the quality-in-use of LORs. It is shown that the created methodology is suitable and stable for evaluating the quality of LOR features and its functionality. A more detail presentation is given on the results of the expert evaluation of the quality-in-use of three LORs that are most popular in Lithuania against the proposed methodology. The novelty of the presented research is achieved through the innovative instrument consisting of the model of the quality-in-use of LORs and the Fuzzy AHP method. The presented methodology could serve as a technological tool for decision making in education as well as in different areas of economy. First published online: 03 Nov 201

Predictive Modelling of Student Academic Performance – the Case of Higher Education in Middle East

One of the main issues in higher education is student retention. Predicting students' performance is an important task for higher education institutions in reducing students' dropout rate and increasing students' success. Educational Data mining is an emerging field that focuses on dealing with data related to educational settings. It includes reading the data, extracting the information and acquiring hidden knowledge. This research used data from one of the Gulf Cooperation Council (GCC) universities, as a case study of Higher Education in the Middle East. The concerned University has an enrolment of about 20,000 students of many different nationalities. The primary goal of this research is to investigate the ability of building predictive models to predict students' academic performance and identify the main factors that influence their performance and grade point average. The development of a generalized model (a model that could be applied on any institution that adopt the same grading system either on the Foundation level (that use binary response variable (Pass/ Fail) or count response variable which is the Grade Average Point for students enrol in the undergraduate academic programs) to identify students in jeopardy of dismissal will help to reduce the dropout rate by early identification of needed academic advising, and ultimately improve students' success. This research showed that data science algorithms could play a significant role in predicting students' Grade Point Average by adopting different regression algorithms. Different algorithms were carried out to investigate the ability of building predictive models to predict students' Grade Point Average after either 2, 4 or 6 terms. These methods are Linear/ Logistic Regression, Regression Trees and Random Forest. These predictive models are used to predict specific students' Grade Point Average based on other values in the dataset. In this type of model, explicit instruction is given about what the model needs to learn. An optimization function (the model) is formed to find the target output based on specific input values. This research opens the door for future comprehensive studies that apply a data science approach to higher-education systems and identifying the main factors that influence student performance

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