Accommodating student's learning experience through personalized learning style adaptation in computer programming course at Centre for Foundation Studies, IIUM

Teaching and learning computer programming can be difficult, especially for beginners. Since they are not exposed to any computer programming experience, they may face difficulties if the teaching approaches do not match with their learning styles. Computer programming requires the students to understand logical reasoning and syntax and be able to apply them practically for solving programming problems in nearly all disciplines. Mitra [1] claims that most students feel computer programming is a challenging intellectual exercise. At Centre for Foundation Studies, foundation students encounter difficulties in learning and applying computer programming concepts. Some of them perform better in other science subjects, but find difficulties in grasping the computer programming concepts. In this research, we have used Felder-Solomon Learning Style Inventory to identify C Programming’s students for their preferred learning styles. The result of the survey shows that the Engineering/Computer Science students came from mixed learning styles. Therefore, we have adapted Felder-Solomon’s learning style model, and come out with a model of three hybrid categories. This paper will provide detail suggestions for an online learning system based on a selected topic in C Programming. The system will accommodate the students’ learning style in accordance to the modified Felder-Solomon’s learning style model. As a significant contribution to programming educations, our suggestions may further be adopted for designing personalized learning for other disciplines

Learning design approaches for personalised and non-personalised e-learling systems

Recognizing the powerful role that technology plays in the lives of people, researchers are increasingly focusing on the most effective uses of technology to support learning and teaching. Technology enhanced learning (TEL) has the potential to support and transform students’ learning and allows them to choose when, where and how to learn. This paper describes two different approaches for the design of personalised and non-personalised online learning environments, which have been developed to investigate whether personalised e-learning is more efficient than non-personalised e-learning, and discuss some of the student’s experiences and assessment test results based on experiments conducted so far

Adaptive Online Module Prototype for Learning Unified Modelling Language (UML)

Every student has their own learning style. Some students may prefer to read the learning material while some other students may prefer to discuss with their colleagues to help them understanding the information more effectively. One of the learning materials is Unified Modeling Language or UML. UML is an industry standard language to specify, visualize, construct, and document the artifacts of software systems. Today, many UML online learning sites are designed to give added information to students other than face-to-face interaction in the classroom. However, most of UML materials provided by the online learning resources are limited to text-based material which is suitable only for students with verbal learning styles. This research aims to identify and analyze the needs before developing a prototype of an adaptive online module. The research is also driven by the need to develop a prototype of adaptive online module that is based on the student's learning style categories in order to help students understand UML better. The result of this research is a prototype of adaptive online module which will identify students' learning styles and lead the students to learning environment that suits their learning style

Teacher/Student Interpersonal Engagement And Customer Service Principles: A Phenomenological Study

The importance of understanding the effects of teacher and student engagement in online learning is especially pertinent to the online teacher and the online student as well as the college as a whole. This qualitative phenomenological study involved discovering if interpersonal online engagement between teacher and student involved those principles used in business customer service. The focus of this phenomenological study is on the lived experience of online students relative to interpersonal engagement – it is about the students’ stories. Accordingly, the questions asked what the participants felt when their teachers were engaged and if the engagement was augmented with customer service principles and, if not, what the participant felt when their instructors did not engage with them using customer service principles. Interpretative Phenomenological Analysis was used to understand and interpret findings. The study involved 10 Associate Degree nurses who were in the RN to Bachelor of Science nursing program. The data was analyzed using NVivo11 by QSR to discover the most emergent themes or patterns in the transcripts. From the collected data, four advantageous themes emerged: personalized professionalism, valued feeling, effective feedback, and good-natured humor. Additionally, there was one disadvantageous theme that emerged: lack of instructor personal approach. The importance of this study lies in the potential for transformation of the online learning environment and community

NON-ADHERENCE TO MEDICATION: A CHALLENGE FOR PERSON-CENTRED PHARMACOTHERAPY TO RESOLVE THE PROBLEM

Pharmacotherapy today is claimed to be fascinating, scientific, rational, and objective, very much evidence-based, powerful and fundamental form of treatment for many medical conditions. Non-adherence to medication as an invisible epidemic is argued to be an Achilles’ heel of evidence based medicine. Person-centered psychiatry has an important role in helping medicine to better understand human nature, human behavior and patients’ choice in complex interactions. Non-adherence is a major target for interventions to improve the quality and outcomes of health care

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Consumer Culture and Purchase Intentions towards Fashion Apparel

This study examines the effectiveness of different fashion marketing strategies and analyzes of the consumer behavior in a cross-section of demographic settings in reference to fashion apparel retailing. This paper also discusses the marketing competencies of fashion apparel brands and retailers in reference to brand image, promotions, and externalmarket knowledge. The study examines the determinants of consumer behavior and their impact on purchase intentions towards fashion apparel. The results reveal that sociocultural and personality related factors induce the purchase intentions among consumers. One of the contributions that this research extends is the debate about the converging economic, cognitive and brand related factors to induce purchase intentions. Fashion loving consumers typically patronage multi-channel retail outlets, designer brands, and invest time and cost towards an advantageous product search. The results of the study show a positive effect of store and brand preferences on developing purchase intentions for fashion apparel among consumers.Consumer behavior, purchase intention, socio-cultural values, designer brands, store brands, fashion apparel, brand promotion, personalization, fashion retailing, psychographic drivers

The relationship between stress and conflict handling style in an ODR environment

Serie : Lecture notes in computer science, vol. 7856, ISSN 0302-9743Up until now, most approaches to Online Dispute Resolution focused on "traditional" problems such as the generation of solutions, the support to negotiation or the definition of strategies. Although these problems are evidently valid and important ones, research should also start to consider new potential issues that arise from technological evolution. In this paper we analyse the new challenges that emerge from resolving conflicts over telecommunications, namely in what concerns the lack of contextual information about parties. Specifically we build on a previous approach to stress estimation from the analysis of interaction and behavioural patterns. From the data gathered in a previous experiment we now trained classifiers that allow to assess stress in realtime, in a personalized and empirical way. With these classifiers, we were able to study how stress and conflict coping strategies evolve together. This paper briefly describes these classifiers, focusing afterwards on the results of the experiment.(undefined