An investigation into university teachers’ and students' perceptions of problem solving in physics in higher education in Saudi Arabia

This study was conducted to investigate university teachers’ and students' perceptions of problem solving in physics in higher education in Saudi Arabia. The current study took into consideration the sociocultural notion that context is an important contributor to the learning process and impacts on the interaction between people. This study focused on aspects of the context, such as community, school, university, language, syllabus and classroom practices, that influence students’ learning of problem-solving in physics. An explanatory sequential mixed methods approach was used to collect data using two questionnaires (the Force Concept Inventory test and the Mechanics Base Line Test), semi-structured interviews, classroom observations and think aloud protocols. The study sample consisted of 31 participants in total, including ten preparatory-year students, eleven first-year students, five preparatory-year teachers and five first-year teachers. The findings revealed that students found difficulty in understanding problems; they did not seem to know how to implement the steps of problem-solving (understanding the problem, devising the plan, carrying out the plan and looking back). Moreover, this study revealed that a number of social and cultural aspects played an essential role in influencing these students’ learning of problem-solving in physics. The study also revealed that students were fearful of asking their teachers questions when they did not understand. Likewise, this study emphasised the important role of providing a safe classroom environment to create social interaction between students and their teachers, and between students themselves, in order to enable students to think and access assistance to their performance, whether from their teacher or peers. Subsequently, this assistance improved students’ understanding in physics lectures and their understanding of physics problems. Also, the study highlighted that a number of linguistic issues, such as the teacher’s dialect or the use of English as medium of instruction, were an obstacle to students’ understanding of mechanics problems, thereby causing an additional cognitive burden. In addition, this study found that students seemed not to have the opportunity to get assistance, such as in the form of feedback or questioning from their teachers, due to the huge number of students in the class, which prevented teachers from guiding students’ thinking while solving physics problems. It was also found that students’ comprehension of Newtonian concepts was inadequate for successful problem-solving due to a lack of basic physics knowledge

Semantics-aware planning methodology for automatic web service composition

Service-Oriented Computing (SOC) has been a major research topic in the past years. It is based on the idea of composing distributed applications even in heterogeneous environments by discovering and invoking network-available Web Services to accomplish some complex tasks when no existing service can satisfy the user request. Service-Oriented Architecture (SOA) is a key design principle to facilitate building of these autonomous, platform-independent Web Services. However, in distributed environments, the use of services without considering their underlying semantics, either functional semantics or quality guarantees can negatively affect a composition process by raising intermittent failures or leading to slow performance. More recently, Artificial Intelligence (AI) Planning technologies have been exploited to facilitate the automated composition. But most of the AI planning based algorithms do not scale well when the number of Web Services increases, and there is no guarantee that a solution for a composition problem will be found even if it exists. AI Planning Graph tries to address various limitations in traditional AI planning by providing a unique search space in a directed layered graph. However, the existing AI Planning Graph algorithm only focuses on finding complete solutions without taking account of other services which are not achieving the goals. It will result in the failure of creating such a graph in the case that many services are available, despite most of them being irrelevant to the goals. This dissertation puts forward a concept of building a more intelligent planning mechanism which should be a combination of semantics-aware service selection and a goal-directed planning algorithm. Based on this concept, a new planning system so-called Semantics Enhanced web service Mining (SEwsMining) has been developed. Semantic-aware service selection is achieved by calculating on-demand multi-attributes semantics similarity based on semantic annotations (QWSMO-Lite). The planning algorithm is a substantial revision of the AI GraphPlan algorithm. To reduce the size of planning graph, a bi-directional planning strategy has been developed