Recently, there has been an increased interest in producing electronic courses. However, literature shows that adopting E-learning does not guarantee improved learning. This is because mixing technology and content does not necessarily yield effective learning. This paper presents a systematic design process for developing blended courses. The instructional design process is based on Bloom Taxonomy, Redeker Taxonomy and Guerra scale. A mapping model is proposed and embedded in the design process to develop a blended course from the objectives and content of a traditional course. This paper also presents an evaluation process that measures the effectiveness of the selected designed blended course. This effectiveness is evaluated in terms of course content formats, interaction and collaboration. A case study is presented to demonstrate the proposed design approach on a System Analysis and Design blended course under development.

Many studies have reported the utilization of Problem-Based Learning (PBL) in teaching Software Engineering courses. However, these studies have different views of the effectiveness of PBL. This paper presents the design of an Advanced Software Engineering course for undergraduate Software Engineering students that uses PBL to teach them Agile software development methods- particularly Scrum. The course also aims to develop entrepreneurial skills needed for software engineering graduates to better prepare them for the software industry. The assessment process designed for this course is illustrated. The paper shows that Scrum practices correlate with the characteristics of the PBL approach, which has resulted in a successful experience of PBL as reported by students in an end of a semester survey.

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E-learning has become one of the powerful supporting tools that expand traditional teaching in higher education. Designers of learning objects (LOs) for blended learning higher education face number of challenges; one of them is choosing the right technology to develop learning objects. This study adopts the Bloom-Redeker-Guerra (B-R-G) mapping model which guides designers to transform the contents and objectives of a traditional course into a number of suggested LOs for a blended course. The study attempts to empirically validate the first dimension of its evaluation scale which measures the effectiveness of learning objects that targets achieving lower order thinking skills (i.e. Knowledge and Comprehension) according to Bloom's Taxonomy. This paper presents the results of the empirical study that validates the students' learning achievement and students' perceived satisfaction differ for receptive learning objects that have been developed with different learning technologies. The empirical study has been implemented using pretest-posttest experiments, in addition to a questionnaire that measures students' satisfaction. Participants were about 100 Information Technology (IT) students enrolled in different courses. Results show that students' learning achievement and students' perceived satisfaction improve with learning objects designed with advanced learning technologies (according to Guerra scale), hence better achieve the targeted learning objectives.

Commuting when there is a significant volume of traffic congestion has been acknowledged as one of the key factors causing stress. Significant levels of stress whilst driving are seen to have a profoundly negative effect on the actions and ability of a driver; this has the capacity to result in risks, hazards and accidents. As such, there is a recognized need to determine drivers’ levels of stress and accordingly predict the key causes responsible for high levels of stress. In this work, the objective is centred on providing an ensemble machine learning framework in order to determine the stress levels of drivers. Moreover, the study also provides a fresh set of data, as gathered from 14 different drivers, with data collection having taken place during driving in Amman, Jordan. Data was gathered via the implementation of a wearable biomedical instrument that was attached to the driver on a continuous basis in order to gather physiological data. The data gathered was accordingly categorised into two different groups: ‘Yes’, which represents the presence of stress, whilst ‘No’ represents the absence of stress. Importantly, in an effort to circumvent the negative impact of driver instances with a minority class on stress predictions, oversampling technique was applied. A two-step ensemble classifier was developed through bringing together the findings from random forest, decision tree, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers, which was then inputted into a Multi-Layer Perceptron neural network. The experimental findings highlight that the suggested framework is far more precise and has a more scalable capacity when compared with all classifiers in relation to accuracy, g-mean measures and sensitivity.