Transforming a linear module into an adaptive one : tackling the challenge

Every learner is fundamentally different. However, few courses are delivered in a way that is tailored to the specific needs of each student. Delivery systems for adaptive educational hypermedia have been extensively researched and found promising. Still, authoring of adaptive courses remains a challenge. In prior research, we have built an adaptive hypermedia authoring system, MOT3.0. The main focus was on enhancing the type of functionality that allows the non-technical author, to efficiently and effectively use such a tool. Here we show how teachers can start from existing course material and transform it into an adaptive course, catering for various learners. We also show how this apparent simplicity still allows for building of flexible and complex adaptation, and describe an evaluation with course authors

Continuous use of authoring for adaptive educational hypermedia : a long-term case study

Adaptive educational hypermedia allows lessons to be personalized according to the needs of the learner. However, to achieve this, content must be split into stand-alone fragments that can be processed by a course personalization engine. Authoring content for this process is still a difficult activity, and it is essential for the popularization of adaptive educational hypermedia that authoring is simplified, so that the various stakeholders in the educational process, students, teachers, administrators, etc. can easily work with such systems. Thus, real-world testing with these stakeholders is essential. In this paper we describe recent extensions and improvements we have implemented in the My Online Teacher MOT3.0 adaptation authoring tool set, based on an initial set of short-term evaluations, and then focus on describing a long-term usage and assessment of the system

Manual and automatic authoring for adaptive hypermedia

Adaptive Hypermedia allows online content to be tailored specifically to the needs of the user. This is particularly valuable in educational systems, where a student might benefit from a learning experience which only displays (or recommends) content that they need to know. Authoring for adaptive systems requires content to be divided into stand-alone fragments which must then be labelled with sufficient pedagogical metadata. Authors must also create a pedagogical strategy that selects the appropriate content depending on (amongst other things) the learner's profile. This authoring process is time-consuming and unfamiliar to most non-technical authors. Therefore, to ensure that students (of all ages, ability level and interests) can benefit from Adaptive Educational Hypermedia, authoring tools need to be usable by a range of educators. The overall aim of this thesis is therefore to identify the ways that this authoring process can be simplified. The research in this thesis describes the changes that were made to the My Online Teacher (MOT) tool in order to address issues such as functionality and usability. The thesis also describes usability and functionality changes that were made to the GRAPPLE Authoring Tool (GAT), which was developed as part of a European FP7 project. These two tools (which utilise different authoring paradigms) were then used within a usability evaluation, allowing the research to draw a comparison between the two toolsets. The thesis also describes how educators can reuse their existing non-adaptive (linear) material (such as presentations and Wiki articles) by importing content into an adaptive authoring system

A tale of two modes : initial reflections on an innovative MOOC

Massive Open Online Courses (MOOCs) are offered by many universities, with hundreds thousands of people worldwide having registered for one or more of the many available courses. Despite the potential that has been claimed for these courses to transform education, in practice the majority are deeply conservative in maintaining the educational status quo. Lacking innovative pedagogic foundation and with the need for approaches that scale, many courses rely heavily on very traditional methods such as mini-lectures and quizzes. In particular, learner support is proving to be insufficient for many participants. This paper reports initial results and experience from developing and presenting a MOOC which provides both “traditional” and supported modes. We present the motivation and objectives for the course, discuss initial results and reflect on lessons learned in the process

Exploring the impact of a flexible, technology-enhanced teaching space on pedagogy

Approaches to teaching and learning are increasingly influenced by the introduction of new technologies and innovative use of space. Recognising the need to keep up to date many institutions has created technology-rich, flexible spaces. Studies so far have concentrated on how students use such facilities; however, their availability also strongly impacts on teaching staff, presenting new possibilities and challenges. To encourage the development of activities that make the most of these resources, the University of Warwick launched the Teaching Grid (2008), a flexible space with state-of-the-art technology. Advisers support colleagues in developing and delivering novel, experimental teaching sessions. This paper reports on use of the facility during its first three years, considering the effects on pedagogy of experimental use of space and technology; this is correlated to an increase in number and variety of teaching and learning activities which, it is suggested, enhances the student experience

A social personalized adaptive e-learning environment : a case study in Topolor

Adaptive e-Learning is a process where learning contents are delivered to learners adaptively, namely, the appropriate contents are delivered to the learners in an appropriate way at an appropriate time based on the learners’ needs, knowledge, preferences and other characteristics. Social e-Learning is a process where connections are made among like-minded learners, so they can achieve learning goals via communication and interaction with each other by sharing knowledge, skills, abilities and materials. This paper reports an extended case study that investigated the influence of social interactions in an adaptive e-Learning environment, by analyzing the usage of social interaction features of a Social Personalized Adaptive E-Learning Environment (SPAEE), named Topolor, which strives to combine the advantages from both social e-Learning and adaptive e-Learning. We present the results of a quantitative case study that evaluates the perceived usefulness and ease of use. The results indicated high satisfaction from the students who were using Topolor for their study and helped us with the evaluation processes. Based on the results, we discuss the follow-up work plan for the further improvements for Topolor

Technology-supported active learning in a flexible teaching space

Active learning is increasingly of interest within Higher Education. The use of technology provides, in theory, the opportunity for more effective active learning, but in practice the majority of learning technology usage is still for “traditional” approaches. Conventional staff training is failing to address this. The authors’ university has provided an experimental technology-rich teaching space (the Teaching Grid) for supporting teachers as they experiment with the delivery of innovative, technology-based teaching. This study investigates teachers’ experiences of trialling active learning approaches within the Teaching Grid using four case studies. The results suggest that the Teaching Grid can be effectively used to support teacher professional development, and the experience of using the facility encourages teachers to integrate technology into their future teaching plans. Five factors are identified which contribute to the promotion of active learning. Teachers’ perceptions of their experience indicate not only the intention to use technology more but also an increased awareness of its potential and openness to adopt more active, student-focused approaches. The broader significance of this work is to identify an alternative model for teacher development which, in contrast to most current approaches, has a demonstrable positive impact on fostering innovative, technology-based pedagogy

Combined cognitive–behavioural and mindfulness programme for people living with dystonia : a proof-of-concept study

Objectives To design and test the delivery of an intervention targeting the non-motor symptoms of dystonia and pilot key health and well-being questionnaires in this population. Design A proof-of-concept study to test the delivery, acceptability, relevance, structure and content for a 3-day group residential programme for the management of dystonia. Setting Participants were recruited from a single botulinum toxin clinic. The intervention was delivered in the community. Participants 14 participants consented to take part (2 withdrew prior to the starting of intervention). The average age was 60 years (range 44–77), 8 of whom were female. After drop-out, 9 participants completed the 3-day programme. Intervention A 3-day group residential programme. Primary and secondary outcome measures Process evaluation and interviews were carried out before and after the intervention to explore participant's views and expectations, as well as experiences of the intervention. Select questionnaires were completed at baseline, 1-month and 3-month follow-up. Results Although participants were not sure what to expect from the programme, they found it informative and for many this together with being in a group with other people with dystonia legitimised their condition. Mindfulness was accepted and adopted as a coping strategy. This was reflected in the 1-month follow-up. Conclusions We successfully delivered a 3-day residential programme to help those living with dystonia manage their condition. Further improvements are suggested. The quantitative outcome measures were acceptable to this group of patients with dystonia

Relative performance of machine learning and linear regression in predicting quality of life and academic performance of school children in Norway : data analysis of a quasi-experimental study

Background: Machine learning (ML) approaches are increasingly being used in health research. It is not clear how useful these approaches are for modelling continuous health outcomes. Child quality of life (QoL) is associated with parental socioeconomic status and child activity levels, and may be associated with aerobic fitness and strength. It is not clear whether diet, or academic performance (AP) is associated with QoL. Objective: To compare predictive performances of ML approaches with linear regression for modelling QoL and AP using parental education and lifestyle data. Methods: We modelled data from children attending nine schools in a quasi-experimental study (NCT02495714). We split data randomly into training and validation sets, and simulated curvilinear, non-linear, and heteroscedastic variables. We examined relative performance of ML approaches using R2, making comparisons to mixed and fixed models, and regression with splines, with and without imputation. We also examined the effect of training set size on overfitting. Results: We had 1,711 cases. Using real data, our regression models explained 24% of AP variance in the complete-case validation set, and up to 15% of QoL variance. While ML models explained high proportions of variance in training sets, in validation sets these explained ~0% of AP and between 3% and 8% of QoL. Following imputation, ML models improved up to 15% for AP. ML models outperformed regression for modelling simulated non-linear and heteroscedastic variables only. A smaller training set did not lead to increased overfitting. The best predictors of QoL were 7-point self-reported activity (P<.001; ß=1.09 (95% CI 0.53 to 1.66)) and TV/computer use (P=.002; ß=-0.95 (-1.55 to -0.36)). For AP, these were mother having master’s-level education (P<.001; ß=1.98 (0.25 to 3.71)) and dichotomised self-reported activity (P=.001; ß=2.47 (1.08 to 3.87)). Adjusted academic performance was associated with QoL (P=.02; ß=0.12 (0.02 to 0.22)). Conclusions: Exercising to cause sweat once per week and 2 hours per day of TV or computer use are associated with small-to-medium increases and decreases in child QoL, respectively. An increase in AP of 20 units is associated with a small increase in QoL. A mother having higher and master’s-level education, 2 hours per day of TV or computer use, and taking at least 2 hours of exercise, are each associated with small-to-medium increases in AP. Differences between effects of computer/TV use for work/leisure needs further investigation. Linear regression is less prone to overfitting and performs better than ML in predicting continuous health outcomes in a dataset containing missing data. Imputation improves ML performance but not enough to outperform regression. ML outperformed regression with non-linear and heteroscedastic data and may be of use when such relationships exist, and where imputation is sensible or there are no missing data. Clinical Trial: The data are from a quasi-experimental design and not an RCT but nevertheless the study from which the data are from does have a registration: NCT0249571

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie