Maximising gain for minimal pain: Utilising natural game mechanics

This paper considers the application of natural games mechanics within higher education as a vehicle to encourage student engagement and achievement of desired learning outcomes. It concludes with desiderata of features for a learning environment when used for assessment and a reflection on the gap between current and aspired learning provision. The context considered is higher (tertiary) education, where the aims are both to improve students’ engagement with course content and also to bring about potential changes in the students’ learning behaviour. Whilst traditional approaches to teaching and learning may focus on dealing with large classes, where the onus is frequently on efficiency and on the effectiveness of feedback in improving understanding and future performance, intelligent systems can provide technology to enable alternative methods that can cope with large classes that preserve the cost-benefits. However, such intelligent systems may also offer improved learning outcomes via a personalised learning experience. This paper looks to exploit particular properties which emerge from the game playing process and seek to engage them in a wider educational context. In particular we aim to use game engagement and Flow as natural dynamics that can be exploited in the learning experience

Metadata for describing learning scenarios under European Higher Education Area paradigm

In this paper we identify the requirements for creating formal descriptions of learning scenarios designed under the European Higher Education Area paradigm, using competences and learning activities as the basic pieces of the learning process, instead of contents and learning resources, pursuing personalization. Classical arrangements of content based courses are no longer enough to describe all the richness of this new learning process, where user profiles, competences and complex hierarchical itineraries need to be properly combined. We study the intersection with the current IMS Learning Design specification and the additional metadata required for describing such learning scenarios. This new approach involves the use of case based learning and collaborative learning in order to acquire and develop competences, following adaptive learning paths in two structured levels

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Application of Particle Swarm Optimization to Formative E-Assessment in Project Management

The current paper describes the application of Particle Swarm Optimization algorithm to the formative e-assessment problem in project management. The proposed approach resolves the issue of personalization, by taking into account, when selecting the item tests in an e-assessment, the following elements: the ability level of the user, the targeted difficulty of the test and the learning objectives, represented by project management concepts which have to be checked. The e-assessment tool in which the Particle Swarm Optimization algorithm is integrated is also presented. Experimental results and comparison with other algorithms used in item tests selection prove the suitability of the proposed approach to the formative e-assessment domain. The study is presented in the framework of other evolutionary and genetic algorithms applied in e-education.Particle Swarm Optimization, Genetic Algorithms, Evolutionary Algorithms, Formative E-assessment, E-education

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

The influence of student characteristics on the use of adaptive e-learning material

Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive elearning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting demographic data and measuring motivation and prior knowledge. We also measured the learning paths students followed and learning strategies they used when working with adaptive e-learning material in a molecular biology course. We then combined these data to study if and how student characteristics relate to the learning paths and strategies they used. We observed that students did follow different learning paths. Gender did not have an effect, but (mainly Dutch) BSc students differed from (international) MSc students in the intrinsic motivation they had and the learning paths and strategies they followed when using the adaptive e-learning materia

The role of unit evaluation, learning and culture dimensions related to student cognitive style in hypermedia learning

Recent developments in learning technologies such as hypermedia are\ud becoming widespread and offer significant contributions to improving the delivery\ud of learning and teaching materials. A key factor in the development of hypermedia\ud learning systems is cognitive style (CS) as it relates to users‟ information\ud processing habits, representing individual users‟ typical modes of perceiving,\ud thinking, remembering and problem solving.\ud \ud \ud \ud \ud A total of 97 students from Australian (45) and Malaysian (52) universities\ud participated in a survey. Five types of predictor variables were investigated with\ud the CS: (i) three learning dimensions; (ii) five culture dimensions; (iii) evaluation\ud of units; (iv) demographics of students; and (v) country in which students studied.\ud Both multiple regression models and tree-based regression were used to analyse\ud the direct effect of the five types of predictor variables, and the interactions within\ud each type of predictor variable. When comparing both models, tree-based\ud regression outperformed the generalized linear model in this study. The research\ud findings indicate that unit evaluation is the primary variable to determine students‟\ud CS. A secondary variable is learning dimension and, among the three dimensions,\ud only nonlinear learning and learner control dimensions have an effect on students‟\ud CS. The last variable is culture and, among the five culture dimensions, only\ud power distance, long term orientation, and individualism have effects on students‟\ud CS. Neither demographics nor country have an effect on students‟ CS.\ud These overall findings suggest that traditional unit evaluation, students‟\ud preference for learning dimensions (such as linear vs non-linear), level of learner\ud control and culture orientation must be taken into consideration in order to enrich\ud students‟ quality of education. This enrichment includes motivating students to\ud acquire subject matter through individualized instruction when designing,\ud developing and delivering educational resources

Virtual pedagogical model: development scenarios

info:eu-repo/semantics/publishedVersio