INTERNET, TELEVISION AND MOBILE TECHNOLOGIES FOR INNOVATIVE ELEARNING

We are developing an innovative cross-media learning delivery system (eBig3, ETM) that goes beyond traditional web-based learning approaches. The approach combines wide availability of television and mobile technologies with the capacity and flexibility of Internet based e-learning. This approach allows the learner to use either a single learning delivery system (depending on availability and preferences) or a complementary combination of two or three delivery systems, thus supporting the anywhere, anytime — by any preference—learning paradigm. The development of the eBig3/ETM learning solutions includes integration of technical aspects of cross-media learning content delivery. Moreover, the approach incorporates pedagogical and usability principles based on an understanding of the target users learning needs and their contexts

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Assessing the benefits of Ajax in mobile learning systems design : a thesis submitted in partial fulfillment of the requirements for a Master of Information Studies at Massey University

Today, mobile technology is rapidly changing our life with increasing numbers of services supported by mobile phones, including mobile Internet access and Web-based mobile learning. The growth of the wireless Internet technology opens new path for people to study in anytime and any location. Using Web-based mobile application to present learning resources for mobile learners is a challenge for developers, because the mobile Internet access performance over GPRS networks is often unacceptably slow. A new Web development model, Ajax, may help to address this problem. Ajax (Asynchronous JavaScript and XML), is a new desktop approach to Web application development that uses client-side scripting to provide a seamless user application experience and reduce traffic between client and server. In this paper, we address the question of whether mobile Ajax provides measurable performance advantages over non-Ajax mobile learning applications. A real-life Web-based mobile learning application performance over a GPRS network study was done based on comparing an Ajax application and an Active Server Pages (ASP) application with identical functionality. Our results suggest that mobile Ajax can reduce the bandwidth requirement by 71%, and cut the server's response time in half. In addition, these performance improvements were noticed by users in our small group usability test

Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets

We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application. They allow for individual budgets given a priori for each test example and for anytime prediction, i.e., a possible interruption at multiple stages during inference while still providing output estimates. Our approach can therefore tackle the computational costs and energy demands of DNNs in an adaptive manner, a property essential for real-time applications. Our Impatient DNNs are based on a new general framework of learning dynamic budget predictors using risk minimization, which can be applied to current DNN architectures by adding early prediction and additional loss layers. A key aspect of our method is that all of the intermediate predictors are learned jointly. In experiments, we evaluate our approach for different budget distributions, architectures, and datasets. Our results show a significant gain in expected accuracy compared to common baselines.Comment: British Machine Vision Conference (BMVC) 201

APP: Anytime Progressive Pruning

With the latest advances in deep learning, there has been a lot of focus on the online learning paradigm due to its relevance in practical settings. Although many methods have been investigated for optimal learning settings in scenarios where the data stream is continuous over time, sparse networks training in such settings have often been overlooked. In this paper, we explore the problem of training a neural network with a target sparsity in a particular case of online learning: the anytime learning at macroscale paradigm (ALMA). We propose a novel way of progressive pruning, referred to as \textit{Anytime Progressive Pruning} (APP); the proposed approach significantly outperforms the baseline dense and Anytime OSP models across multiple architectures and datasets under short, moderate, and long-sequence training. Our method, for example, shows an improvement in accuracy of $\approx 7\%$ and a reduction in the generalization gap by $\approx 22\%$, while being $\approx 1/3$ rd the size of the dense baseline model in few-shot restricted imagenet training. We further observe interesting nonmonotonic transitions in the generalization gap in the high number of megabatches-based ALMA. The code and experiment dashboards can be accessed at \url{https://github.com/landskape-ai/Progressive-Pruning} and \url{https://wandb.ai/landskape/APP}, respectively.Comment: 21 pages including 4 pages of references. Preprint versio

Automatic Algorithm Selection for Pseudo-Boolean Optimization with Given Computational Time Limits

Machine learning (ML) techniques have been proposed to automatically select the best solver from a portfolio of solvers, based on predicted performance. These techniques have been applied to various problems, such as Boolean Satisfiability, Traveling Salesperson, Graph Coloring, and others. These methods, known as meta-solvers, take an instance of a problem and a portfolio of solvers as input. They then predict the best-performing solver and execute it to deliver a solution. Typically, the quality of the solution improves with a longer computational time. This has led to the development of anytime selectors, which consider both the instance and a user-prescribed computational time limit. Anytime meta-solvers predict the best-performing solver within the specified time limit. Constructing an anytime meta-solver is considerably more challenging than building a meta-solver without the "anytime" feature. In this study, we focus on the task of designing anytime meta-solvers for the NP-hard optimization problem of Pseudo-Boolean Optimization (PBO), which generalizes Satisfiability and Maximum Satisfiability problems. The effectiveness of our approach is demonstrated via extensive empirical study in which our anytime meta-solver improves dramatically on the performance of Mixed Integer Programming solver Gurobi, which is the best-performing single solver in the portfolio. For example, out of all instances and time limits for which Gurobi failed to find feasible solutions, our meta-solver identified feasible solutions for 47% of these

FLIPPED CLASSROOM FOR UNDERGRADUATE INTRODUCTORY BIOMECHANICS CLASS

The purpose of this exploratory study was to examine the effectiveness of flipped classroom teaching approach in an undergraduate introductory biomechanics class. A total of 28 students were recruited for the study. Students were required to watch short videos, study reading assigned, and complete pre-work assignments before each class. During class time, students were mainly engaged with problem-based learning. The biomechanics concept inventory (BCI) version 3 was used to determine learning improvement. The pre-, post-test, and survey were administered during the first and last two weeks of the semester. Students demonstrated significant learning improvement (d = 1.23, P \u3c 0.05) with a 23% normalised learning gain. Most students (64%) indicated that the flipped classroom approach works well for their learning because it provides flexibility that they may review the course lesson anytime on their own

Applied learning in online spaces: Traditional pedagogies informing educational design for today's learners

The challenge to provide engaging, effective learning environments for university students is perhaps greater now than ever before. While the ‘anytime, anywhere’ online learning environment appeals, students also need a learning environment that encourages and retains their engagement. A new teacher-education program with an explicit focus on applied learning commenced at the University of Tasmania in 2011. The fully online course aims to provide an authentic, engaging environment for the students, who are primarily mature-aged, in-service teachers in TAFE colleges. This paper describes the applied learning design principles created to guide the course development and delivery, and the initial findings of a doctoral study being undertaken to examine their effectiveness. The research aims to provide a set of tested design principles to encourage and support an applied learning approach in online teacher-education courses, and more broadly in higher education