Articulation of information requirements for personalised knowledge and construction

The quality of information provision influences considerably knowledge construction driven by individual users’ needs. In the design of information systems for e-learning, personal information requirements should be incorporated to determine a selection of suitable learning content, instructive sequencing for learning content, and effective presentation of learning content. This is considered as an important part of instructional design for a personalised information package. The current research reveals that there is a lack of means by which individual users’ information requirements can be effectively incorporated to support personal knowledge construction. This paper presents a method which enables an articulation of users’ requirements based on the rooted learning theories and requirements engineering paradigms. The user’s information requirements can be systematically encapsulated in a user profile (i.e. user requirements space), and further transformed onto instructional design specifications (i.e. information space). These two spaces allow the discovering of information requirements patterns for self-maintaining and self-adapting personalisation that enhance experience in the knowledge construction process.

Mars: a personalised mobile activity recognition system

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today’s smart phones. The state of the art in mobile activity recognition uses traditional classification learning techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository; ii) model building where the classification model is trained and tested using the collected data; iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables promptly adaptation. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practise that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources.

Context-Aware Personalised Service Delivery

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Bootcamp EMMA MOOC Assessment for learning in practice

Formative assessment or assessment for learning is a relevant theme for teachers and educationalists. Formative assessment is a valuable tool for supporting the learning process. It is applied during learning and offers you more and better opportunities to guide your students. Formative assessment allows for more individual and/or personalised guidance. In this MOOC Assessment for learning in practice we will provide you with theory and guidelines for knowledge construction on the topic of formative assessment while offering support in designing assessments that can be applied as a tool for learning and training of competences. In this MOOC you can learn what formative assessment is, learn to differentiate between summative and formative assessment, and how formative assessment can contribute to the learning of your pupils or students. Design of rubrics, the role and functions of feedback, the use of technology for formative assessment are the topics of the MOOC.

Is Our Students Learning - QuestionMark: Extending E-Assessment Systems for Adapative Marking and Feedback

Our key contribution is a flexible, automated marking system that adds desirable functionality to existing E-Assessment systems. In our approach, any given E-Assessment system is relegated to a data-collection mechanism, whereas marking and the generation and distribution of personalised per-student feedback is handled separately by our own system. This allows content-rich Microsoft Word feedback documents to be generated and distributed to every student simultaneously according to a per-assessment schedule.

The feedback is adaptive in that it corresponds to the answers given by the student and provides guidance on where they may have gone wrong. It is not limited to simple multiple choice which are the most prescriptive question type offered by most E-Assessment Systems and as such most straightforward to mark consistently and provide individual per-alternative feedback strings. It is also better equipped to handle the use of mathematical symbols and images within the feedback documents which is more flexible than existing E-Assessment systems, which can only handle simple text strings.

As well as MCQs the system reliably and robustly handles Multiple Response, Text Matching and Numeric style questions in a more flexible manner than Questionmark: Perception and other E-Assessment Systems. It can also reliably handle multi-part questions where the response to an earlier question influences the answer to a later one and can adjust both scoring and feedback appropriately.

New question formats can be added at any time provided a corresponding marking method conforming to certain templates can also be programmed. Indeed, any question type for which a programmatic method of marking can be devised may be supported by our system. Furthermore, since the student’s response to each is question is marked programmatically, our system can be set to allow for minor deviations from the correct answer, and if appropriate award partial marks.

Bespoke Radiology e-Learning

Introduction: Foundation doctors are expected to assess and interpret plain x-ray studies of the chest/abdomen before a definitive report is issued by senior staff. The Royal College of Radiologists have published guidelines (RCR curriculum) on the scope of plain film findings medical students should be familiar with.1 Studies have shown that the x-ray interpretation without feedback does not significantly improve diagnostic ability. 2 Queen’s University, Belfast Trust Radiology and Experior Medical developed an online system to assess individual student ability to interpret X-ray findings. Over a series of assessments each student’s profile is built up, identifying strengths and weakness. The system can then create bespoke individual assessments re-evaluating previously identified weak areas and quantifying interpretative skill improvement. Aim: To determine how readily an online system is adopted by senior medical students, investigating if increasing exposure to x-ray interpretation combined with cyclical formative feedback enhances performance. Methods: The system was offered to all 270 final year medical students as an online resource. The system comprised a series of 20 weekly 30 minute assessments, containing normal and abnormal x-rays within the RCR curriculum. After each assessment students were given formative feedback, including their own result, annotated answers, peer group comparison and a breakdown of areas of strength and weakness. Focus groups of 4-5 students addressed student perspectives of the system, including ease of use, image resolution, system performance across different operating platforms, perceived value of formative feedback loops, breakdown of performance and the value of bespoke personalised assessments. Research Ethics Approval was granted for the study. Data analysis was via two-sided one-sample t-test; initial minimal recruitment was estimated as 60 students, to detect a mean 10% change in performance, with a standard deviation of 20%. Results and Discussion: Over 80% (n = XXX/270) of the student cohort engaged with the study. Student baseline average was 39%, increasing to 62% by the exit test. The steadily sustained improvement (57% relative performance in interpretative diagnostic accuracy) was despite increasing test difficulty. Student feedback via focus groups was universally positive throughout the examined domains. Conclusion: The online resource proved to be valuable, with high levels of student engagement, improving performance despite increasingly difficulty testing and positive learner experience with the system. References: 1. Undergraduate Radiology Curriculum, The Royal College of Ra, April 2012. Ref No. BFCR(12)4 The Royal College of Radiologists, April 2012 2. I Satia, S Bashagha, A Bibi, R Ahmed, S Mellor, F Zaman. Assessing the accuracy and certainty in interpretating chest x-rays in the medical division. Clin Med August 2013 Vol.13 no. 4 349-352