Affect-Based Effects of Simulation Games on Learning

Empirical evidence has demonstrated the benefits of using simulation games in enhancing learning especially in terms of cognitive gains. This is to be expected as the dynamism and non-linearity of simulation games are more cognitively demanding. However, the other effects of simulation games, specifically in terms of learners’ emotions, have not been given much attention and are under-investigated. This study aims to demonstrate that simulation games stimulate positive emotions from learners that help to enhance learning. The study finds that the affect-based constructs of interest, engagement and appreciation are positively correlated to learning. A stepwise multiple regression analysis shows that a model involving interest and engagement are significantly associated with learning. The emotions of learners should be considered in the development of curriculum, and the delivery of learning and teaching as positive emotions enhances learning.

From Kandinsky to Java (The Use of 20th Century Abstract Art in Learning Programming)

At the University of Worcester we are continually striving to find new approaches to the learning and teaching of programming, to improve the quality of learning and the student experience. Over the past three years we have used the contexts of robotics, computer games, and most recently a study of Abstract Art to this end. This paper discusses our motivation for using Abstract Art as a context, details our principles and methodology, and reports on an evaluation of the student experience. Our basic tenet is that one can view the works of artists such as Kandinsky, Klee and Malevich as Object-Oriented (OO) constructions. Discussion of these works can therefore be used to introduce OO principles, to explore the meaning of classes, methods and attributes and finally to synthesize new works of art through Java code. This research has been conducted during delivery of an “Advanced OOP (Java)” programming module at final-year Undergraduate level, and during a Masters’ OO-Programming (Java) module. This allows a comparative evaluation of novice and experienced programmers’ learning. In this paper, we identify several instructional factors which emerge from our approach, and reflect upon the associated pedagogy. A Catalogue of ArtApplets is provided at the associated web-site.

Common Environment for Undergraduate Computer Programming

We present an Integrated Environment suitable for learning and teaching computer programming which is designed for both students of specialised Computer Science courses, and also non-specialist students such as those following Liberal Arts. The environment is rich enough to allow exploration of concepts from robotics, artificial intelligence, social science, and philosophy as well as the specialist areas of operating systems and the various computer programming paradigms.

Confluent Learning: Using a Design Approach to Develop Cognitive Abilities and Enhance Affective Capacities Through Change Management Curriculum

Purpose The aim of the study is to explore the role of confluent learning in supporting the development of change management knowledge, skills and attitudes and to inform the creation of a conceptual model based upon a priori and a posteriori knowledge gained from literature and the research. Design/methodology/approach The research adopts qualitative approach based on reflective inquiry methodology. There are two primary data sources, interviews with learners and the researchers’ reflective journals on learners’ opinions. Findings The confluent learning approach helped to stimulate affective states (e.g. interest and appreciation) to further reinforce cognitive gains (e.g. retention of knowledge) as a number of higher order thinking skills were further developed. The instructional design premised upon confluent learning enabled learners to further appreciate the complexities of change management. Research implications/ limitations The confluent learning approach offers another explanation to how learning takes place, contingent upon the use of a problem solving framework, instructional design and active learning in developing inter- and trans-disciplinary competencies. Practical implications This study not only explains how effective learning takes place but is also instructive to learning and teaching, and human resource development (HRD) professionals in curriculum design and the potential benefits of confluent learning. Social implications The adoption of a confluent learning approach helps to re-naturalise learning that appeals to learners affect. Originality/value This research is one of the few studies that provide an in-depth exploration of the use of confluent learning and how this approach co-develops cognitive abilities and affective capacity in the creation of a conceptual model.

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.