The higher education landscape in developing countries is faced with many challenges, one of which is high faculty to student ratio. An obvious implication of this is compromise on the quality of classroom engagement. The distractions caused by the not conducive learning space and instructors' inability to elucidate correct feedbacks from students usually lead to poor learning outcomes. Feedback mechanisms that are unobtrusive and efficient in processing large data in real-time are needful to measure quality learning experience in such large classroom settings. With the latest impact of penetration and adoption of internet and mobile technologies in most developing counties, wearable technology is a feasible solution to manage and monitor classroom involvement; as real time student feedback can be integrated in the design and delivery of instruction in and out of the classroom. In this paper, we present state of the art of wearable technology and explored the opportunities of wearable technology in the higher education. Specifically, we presented scenarios in which wearable technology can be employed to understand and analyze physiological signals and emotional responses from learners in real-time; the end result of which would increase the quality of classroom engagement, inspire new pedagogy, drive new trends in peer-to-peer collaborations, and increase the learning outcomes. Moreover, we identified some challenges that may hinder this development such as: inconclusive user studies of wearable technology in developing countries and inadequate infrastructure. Finally, we make appropriate recommendations on how these challenges can be surmounted

The plethora of cloud application services (Apps) in the cloud business apps e-marketplace often leads to service choice overload. Meanwhile, existing SaaS e-marketplaces employ keyword-based inputs that do not consider both the quantitative and qualitative quality of service (QoS) attributes that characterise cloud-based services. Also, existing QoS-based cloud service ranking approaches rank cloud application services are based on the assumption that the services are characterised by quantitative QoS attributes alone, and have employed quantitative-based similarity metrics for ranking. However, the dimensions of cloud service QoS requirements are heterogeneous in nature, comprising both quantitative and qualitative QoS attributes, hence a cloud service ranking approach that embrace core heterogeneous QoS dimensions is essential in order to engender more objective cloud selection. In this paper, we propose the use of heterogeneous similarity metrics (HSM) that combines quantitative and qualitative dimensions for QoS-based ranking of cloud-based services. By using a synthetically generated cloud services dataset, we evaluated the ranking performance of five HSM using Kendall tau rank coefficient and precision as accuracy metrics benchmarked with one HSM. The results show significant rank order correlation of Heterogeneous Euclidean-Eskin Metric, Heterogeneous Euclidean-Overlap Metric, and Heterogeneous Value Difference Metric with human similarity judgment, compared to other metrics used in the study. Our results confirm the applicability of HSM for QoS ranking of cloud services in cloud service e-marketplace with respect to users' heterogeneous QoS requirements.

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