Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Psychometrics in Practice at RCEC

A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment

Case studies of personalized learning

Deliverable 4.1, Literature review of personalised learning and the Cloud, started with an evaluation and synthesis of the definitions of personalized learning, followed by an analysis of how this is implemented in a method (e-learning vs. i-learning, m-learning and u-learning), learning approach and the appropriate didactic process, based on adapted didactic theories. From this research a list of criteria was created needed to implement personalised learning onto the learner of the future. This list of criteria is the basis for the analysis of all case studies investigated. – as well to the learning process as the learning place. In total 60 case studies (all 59 case studies mentioned in D6.4 Education on the Cloud 2015 + one extra) were analysed. The case studies were compared with the list of criteria, and a score was calculated. As a result, the best examples could be retained. On average most case studies were good on: taking different learning methods into account, interactivity and accessibility and usability of learning materials for everyone. All had a real formal education content, thus aiming at the core-curriculum, valuing previous knowledge, competences, life and work skills, also informal. Also the availability of an instructor / tutor or other network of peers, experts and teachers to guide and support the learning is common. On the other hand, most case studies lack diagnostics tests as well at the start (diagnostic entry test), during the personalized learning trajectory and at the end (assessment at the end). Also most do not include non-formal and informal learning aspects. And the ownership of personalized learning is not in the hands of the learner. Five of the 60 case studies can as a result be considered as very good examples of real personalized learning

Purposive Teaching Styles for Transdisciplinary AEC Education: A Diagnostic Learning Styles Questionnaire

With the progressive globalisation trend within the Architecture, Engineering, and Construction (AEC) industry, transdisciplinary education and training is widely acknowledged as being one of the key factors for leveraging AEC organisational success. Conventional education and training delivery approaches within AEC therefore need a paradigm shift in order to be able to address the emerging challenges of global practices. This study focuses on the use of Personalised Learning Environments (PLEs) to specifically address learners’ needs and preferences (learning styles) within managed Virtual Learning Environments (VLEs). This research posits that learners can learn better (and be more readily engaged in managed learning environments) with a bespoke PLE, in which the deployment of teaching and learning material is augmented towards their individual needs. In this respect, there is an exigent need for the Higher Educational Institutions (HEIs) to envelop these new approaches into their organisational learning strategy. However, part of this process requires decision-makers to fully understand the core nuances and interdependencies of functions and processes within the organisation, along with Critical Success Factors (CSFs) and barriers. This paper presents findings from the development of a holistic conceptual Diagnostic Learning Styles Questionnaire (DLSQ) Framework, comprised of six interrelated dependencies (i.e. Business Strategy, Pedagogy, Process, Resources, Systems Development, and Evaluation). These dependencies influence pedagogical effectiveness. These finding contribute additional understanding to the intrinsic nature of pedagogy in leveraging transdisciplinary AEC training within organisations (to improve learner effectiveness). This framework can help organisations augment and align their strategic priorities to learner-specific traits

Designing intelligent computer‐based simulations: A pragmatic approach

This paper examines the design of intelligent multimedia simulations. A case study is presented which uses an approach based in part on intelligent tutoring system design to integrate formative assessment into the learning of clinical decision‐making skills for nursing students. The approach advocated uses a modular design with an integrated intelligent agent within a multimedia simulation. The application was created using an object‐orientated programming language for the multimedia interface (Delphi) and a logic‐based interpreted language (Prolog) to create an expert assessment system. Domain knowledge is also encoded in a Windows help file reducing some of the complexity of the expert system. This approach offers a method for simplifying the production of an intelligent simulation system. The problems developing intelligent tutoring systems are examined and an argument is made for a practical approach to developing intelligent multimedia simulation systems

An open learning environment for the diagnosis, assistance and evaluation of students based on artificial intelligence

The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results

Towards an Integrative Formative Approach of Data-Driven Decision Making, Assessment for Learning, and Diagnostic Testing

This study concerns the comparison of three approaches to assessment: Data-Driven Decision Making, Assessment for Learning, and Diagnostic Testing. Although the three approaches claim to be beneficial with regard to student learning, no clear study into the relationships and distinctions between these approaches exists to date. The goal of this study was to investigate the extent to which the three approaches can be shaped into an integrative formative approach towards assessment. The three approaches were compared on nine characteristics of assessment. The results suggest that although the approaches seem to be contradictory with respect to some characteristics, it is argued that they could complement each other despite these differences. The researchers discuss how the three approaches can be shaped into an integrative formative approach towards assessmen