Students' feedback is an effective mechanism that provides valuable insights about teachinglearning process. Handling opinions of students expressed in reviews is a quite labour-intensive and tedious task as it is typically performed manually by the human intervention. While this task may be viable for smallscale courses that involve just a few students' feedback, it is unpractical for large-scale cases as it applies to online courses in general, and MOOCs, in particular. Therefore, to address this issue, we propose in this paper a framework to automatically analyzing opinions of students expressed in reviews. Specifically, the framework relies on aspect-level sentiment analysis and aims to automatically identify sentiment or opinion polarity expressed towards a given aspect related to the MOOC. The proposed framework takes advantage of weakly supervised annotation of MOOC-related aspects and propagates the weak supervision signal to effectively identify the aspect categories discussed in the unlabeled students' reviews. Consequently, it significantly reduces the need for manually annotated data which is the main bottleneck for all deep learning techniques. A large-scale real-world education dataset containing around 105k students' reviews collected from Coursera and a dataset comprising of 5989 students' feedback in traditional classroom settings are used to perform experiments. The experimental results indicate that our proposed framework attains inspiring performance with respect to both the aspect category identification and the aspect sentiment classification. Moreover, the results suggest that the framework leads to more accurate results than the expensive and labour-intensive sentiment analysis techniques relying heavily on manually labelled data.

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In this paper, we present our design approach for bridging outdoors and indoors learning activities with the support of mobile and positioning technologies. In order to illustrate these research efforts we describe the outcomes of two trials we have conducted with more than 50 elementary school children. The activities presented in this paper aspire at supporting the notion of situated learning with mobile and positioning technologies to promote new ways of collaboration based on the users' learning context. The results of our experiments indicate that children enjoyed learning where mobile devices are used in situ, supporting the learning activities in the context of which they are taking place.Reference to this paper should be made as follows: Kurti, A., Spikol, D. and Milrad, M. (2008) 'Bridging outdoors and indoors educational activities in schools with the support of mobile and positioning technologies', Int.

Continuous change changes everything; it introduces various uncertainties, which may harm the sustainability of software systems. We argue that integrating runtime adaptation and evolution is crucial for the sustainability of software systems. Realising this integration calls for a radical change in the way software is developed and operated. Our position is that we need to Design for Sustainability. To that end, we present: (i) the AdEpS model (Adaptation and Evolution processes for Sustainability) to handle and mitigate uncertainties by means of integrating runtime adaptation and evolution, and (ii) a set of engineering principles to design software systems that facilitate the application of the AdEpS model to build sustainable software.

The advent of MOOC platforms brought an abundance of video educational content that made the selection of best fitting content for a specific topic a lengthy process. To tackle this challenge in this paper we report our research e↵orts of using deep learning techniques for managing and classifying educational content for various search and retrieval applications in order to provide a more personalized learning experience. In this regard, we propose a framework which takes advantages of feature representations and deep learning for classifying video lectures in a MOOC setting to aid e↵ective search and retrieval. The framework consists of three main modules. The first module called pre-processing concerns with video-to-text conversion. The second module is transcript representation which represents text in lecture transcripts into vector space by exploiting di↵erent representation techniques including bag-of-words, embeddings, transfer learning, and topic modeling. The final module covers classifiers whose aim is to label video lectures into the appropriate categories. Two deep learning models, namely feed-forward deep neural network (DNN) and convolutional neural network (CNN) are examined as part of the classifier module. Multiple simulations are carried out on a large-scale real dataset using various feature representations and classification techniques to test and validate the proposed framework.

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This paper presents the design, implementation and the overall lifecycle of a software system that includes mobile and web components and that evolved having the following aspects in mind: (1) System Requirements and Architectural Design, (2) System Implementation and Deployment, and (3) System Assessment and Usability Testing. During the three years of development efforts three software prototypes were implemented utilizing service-oriented approaches. These efforts have been tested with more than 200 users during this period. The outcomes of these activities led to the design and implementation of a system architecture that relies on serviceoriented approaches and open standards. Moreover, extensive prototyping with incremental development stages helped to find the balance between the design and implementation of the system while reflecting to rapid changes of software and webbased technologies. Finally, user testing for assessment and testing of the software system were employed in order to cope with the dynamic user requirements. The main outcomes of the efforts described in this paper are presented and summarized in the form of Architectural Concepts that pave the way towards an open, extensible architecture.

Software development today is based on a set of Agile approaches in the mindset of Lean. These emphasize the need for team collaboration and communication, rapid feedback and continuous learning. This creates the need for software intensive companies to educate their developers to these ways of working in a manner that allows fast adoption of the acquired skills also in professional capacity. As Agile emphasizes learning, teaching agile development can rely on modern, learner-centric approaches such as situated learning and the idea of a flipped classroom. In this paper we present and reflect upon a case study of two courses of teaching distributed agile development for software professionals based on modern learning theories engaging the learners directly in practice.