Applying web-mediated co-curricular learning and phenomenon-based learning to improve students’ programming skills and self-efficacy in an online programming course

Universal Access in the Information Society - Aiming to develop practical programming skills and self-efficacy of students in an online learning environment, two teaching approaches were integrated...     

Students' self-report and observed learning orientations in blended university course design: How are they related to each other and to academic performance?

This study examines the extent to which the learning orientations identified by student self-reports and the observation of their online learning events were related to each other and to their academic performance. The participants were 322 first-year engineering undergraduates, who were enrolled in a blended course. Using students' self-report on a questionnaire about their approaches to learning and perceptions of the blended learning environment, ‘understanding’ and ‘reproducing’ learning orientations were identified. Using observations of student activity online, a Hidden Markov Model (HMM) and agglomerative sequence clustering detected four qualitatively different patterns of online learning orientations. Cross-tabulations showed significant and logical associations amongst the learning orientations derived by the self-report and observational methods. Significant differences were also consistently found in the students' academic performance across the mid-term and final assessments based on their learning orientations detected by both self-report and observational methods, results which have important implications for learning research.     

“But my subject's different”: a web-based approach to supporting disciplinary lifelong learning skills

Many new initiatives in Higher Education institutions choose to develop web sites to support their work, not least because web-based delivery of support materials from a central unit can help to deliver development materials via a single point of access, and ‘on demand’. But this presents its own difficulties in terms of the selection and structure of generic material, and in making students aware of its existence. In this paper, the problem of designing a centrally managed web site (both in terms of structure and format) that adequately supports students across the institution will be discussed, and a strategy for developing a site that meets departmental needs will be presented, together with a discussion of the impact of this approach on the role of the developer. This is illustrated within the context of supporting Key Skills. ‘Key’ or ‘transferable’ skills are now recognised as being essential for most people in work and in life. Development of these skills is being encouraged at every level in education, and is demanded with increasing frequency by employers and professional bodies. Within Higher Education, the skills debate has prompted an examination of how students manage their own learning, and skills development initiatives encourage learners to seek ways of filling gaps in their knowledge and experience. However, at university level, it is frequently the case that explicit development of key skills must fall to students' spare time or to their extra-curricular activities. This is partly explained by departmental traditions and experience, which may not encompass skills development. The UCL Key Skills site model, based on a ‘core’ website with customised departmental homepages, allows departments to make best use of central resources by ‘personalising’ the routes into these for their students. It also draws an important distinction in terms of the purpose of the web site, between management of information (for the central site) and pedagogy (for the departmental pages). The model helps to lay the foundations for graduates' lifelong learning by encouraging skills development, within a discipline-specific setting. The benefits of this approach—which included greatly increased levels of engagement and raised awareness amongst staff—are illustrated by a case study from the UCL Geography department, and wider applications of the model as a way of supporting centralised initiatives are discussed.     

Can more interactivity improve learning achievement in an online course? Effects of college students' perception and actual use of a course-management system on their learning achievement

This study aims to investigate how interactivity influence learners' use of interactive functions in the course-management system (CMS) and their online learning performance. A two-tier mediation framework is proposed to examine the mediating effects of different actual-use records concerning the CMS's interactive functions. Data are collected from 381 undergraduate students who enrolled in a general-education asynchronous online course from three universities in Taiwan. The results indicate that the relationships among students' self-reported use of interactive functions, students' perceptions of the usefulness of interactive functions, and students' actual-use logs have some direct influences on students' online learning performance (online-discussion scores, exam scores, and group-project scores). In addition, students' actual-use logs (the number of times of log-ins to the online course, the number of times students read learning materials, and the number of postings on the discussion board) have a mediated effect on students' self-reported frequency of logging into the CMS, students' self-reported frequency of using the learner–instructor/learner–learner interactive functions, and online learning performance. The findings and implications could constitute a useful guide for educational practitioners and designers concerned with the effective integration of interactivity into future online courses.     

The role of “craft language” in learning “Waza”

The role of craft language in the process of teaching (learning) Waza (skill) will be discussed from the perspective of human intelligence.It may be said that the ultimate goal of learning Waza in any Japanese traditional performance is not the perfect reproduction of the teaching (learning) process of Waza. In fact, a special metaphorical language (craft language) is used, which has the effect of encouraging the learner to activate his creative imagination. It is through this activity that the he learns his own habitus (Kata).It is suggested that, in considering the difference of function between natural human intelligence and artificial intelligence, attention should be paid to the imaginative activity of the learner as being an essential factor for mastering Kata.This article is a modified English version of Chapter 5 of my bookWaza kara shiru (Learning from Skill), Tokyo University Press, 1987, pp. 93–105.     

Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2?% when compared to the state-of-the-art Convolutional Neural Network (CNN) models.

     

Cognitive load theory vs. constructivist approaches: which best leads to efficient,deep learning?

Computer‐assisted learning, in the form of simulation‐based training, is heavily focused upon by the military. Because computer‐based learning offers highly portable, reusable, and cost‐efficient training options, the military has dedicated significant resources to the investigation of instructional strategies that improve learning efficiency within this environment. In order to identify efficient instructional strategies, this paper investigates the two major learning theories that dominate the recent literature on optimizing knowledge acquisition: cognitive load theory (CLT) and constructivism. According to CLT, instructional guidance that promotes efficient learning is most beneficial. Constructivist approaches, in contrast, emphasize the importance of developing a conceptual understanding of the learning material. Supporters of these theories have debated the merits and shortcomings of both positions. However, in the absence of consensus, instructional designers lack a well‐defined model for training complex skills in a rapid, efficient manner. The current study investigates the relative utility of CLT and constructivist‐based approaches for teaching complex skills using a military command and control task. Findings suggest that the acquisition of procedural, declarative, and conceptual knowledge, as well as decision‐making skills, did not differ as a function of the type of instruction used. However, integrated knowledge was slightly better retained by the group provided with CLT‐based instruction. These results are contrary to our expectation that constructivist approaches, which focus on the development and integration of information, would yield better performance in an applied problem‐based environment. Thus, while contemporary researchers continue to defend the use of constructivist strategies for teaching, our research supports earlier findings that question the utility, efficiency, and impact of these strategies in applied domains.     

Students’ multimodal knowledge practices in a makerspace learning environment

International Journal of Computer-Supported Collaborative Learning - In this study, we aim to widen the understanding of how students’ collaborative knowledge practices are mediated...     

Small business in a small country: Attitudes to “Green” IT

New Zealand is a small country and most of its businesses are small with 96 % of all enterprises employing fewer than 20 people. New Zealand takes pride in its “clean green” image, however small and medium sized businesses (SMEs) have limited resources, low awareness of the environmental impacts of the technologies they use, and perceive relatively few imperatives for changing their practices. In this paper we use institutional theory to frame a study of the factors which influence SMEs to adopt Green IS/IT practices. We found a diversity of viewpoints that suggest that adopting a one size fits all approach around encouraging organizations to adopt Green IS/IT is unlikely to be successful. However, it is doubtful that leadership in this area will arise in the small business sector, and coercive pressure from Government or other regulatory bodies is likely to be required. Market-place imperatives from customers and large players in supply chains can also be significant. Institutional theory was further used to understand the relative importance of these different pressures. These findings have relevance in other contexts as SMEs are the dominant form of business in many countries around the world and SMEs are estimated to have a combined contribution of around 70 % in terms of global pollution.     

Using Project-Based-Learning in a mobile application development course—An experience report

In this paper, we report the experience gained in a Mobile Application Development course. We involved students in Computer Science at the University of Salerno, who in teams had to conduct a project. The goal of this project was to design and develop applications (or simply app) for Android-based devices. The adopted teaching approach was based on Project-Based-Learning and enhanced collaboration and competition. Collaboration took place among members of the same team (intra-team), while competition among different teams of students (extra-team). To allow intra-team collaboration, students used GitHub as Computer-Supported-Collaborative-Learning tool. It provided support for implicit and explicit communication among members in each team and for distributed revision control and management of software artifacts (e.g., source code and requirements models). Developed apps underwent a final public competition prized by IT managers of national and international software companies. This is how we implemented extra-team competition. IT managers expressed a positive judgment on both students׳ competition and developed apps. Also, students provided very good feedback on used teaching approach and support GitHub provided.     

How to find people who can help to answer a question? – Analyses of metrics and machine learning in online communities

Online communities have become important places for users to exchange information and build knowledge. In these communities, people ask and answer questions, learn with each other, but some problems may occur such as not getting an answer or getting contradictory ones. In order to increase the responsiveness of the communities, it would be important to identify people who are willing to help and who provide good answers in such communities, whom we call reliable users. We investigated various components of online communities and users’ attributes looking for a correlation between these characteristics and the users’ reputation in these communities. After that, we proposed the usage of two machine learning techniques, artificial neural network and clustering algorithm, with the users’ attributes for finding reliable sources. The results show that the usage of an artificial neural network is a good approach as around 90% of the users were correctly identified while the clustering algorithm makes to find groups of reliable users more easily.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

Message classification as a basis for studying command and control communications—an evaluation of machine learning approaches

In military command and control, success relies on being able to perform key functions such as communicating intent. Most staff functions are carried out using standard means of text communication. Exactly how members of staff perform their duties, who they communicate with and how, and how they could perform better, is an area of active research. In command and control research, there is not yet a single model which explains all actions undertaken by members of staff well enough to prescribe a set of procedures for how to perform functions in command and control. In this context, we have studied whether automated classification approaches can be applied to textual communication to assist researchers who study command teams and analyze their actions. Specifically, we report the results from evaluating machine leaning with respect to two metrics of classification performance: (1) the precision of finding a known transition between two activities in a work process, and (2) the precision of classifying messages similarly to human researchers that search for critical episodes in a workflow. The results indicate that classification based on text only provides higher precision results with respect to both metrics when compared to other machine learning approaches, and that the precision of classifying messages using text-based classification in already classified datasets was approximately 50%. We present the implications that these results have for the design of support systems based on machine learning, and outline how to practically use text classification for analyzing team communications by demonstrating a specific prototype support tool for workflow analysis.     

How to “alternatize” a clustering algorithm

Given a clustering algorithm, how can we adapt it to find multiple, nonredundant, high-quality clusterings? We focus on algorithms based on vector quantization and describe a framework for automatic ‘alternatization’ of such algorithms. Our framework works in both simultaneous and sequential learning formulations and can mine an arbitrary number of alternative clusterings. We demonstrate its applicability to various clustering algorithms—k-means, spectral clustering, constrained clustering, and co-clustering—and effectiveness in mining a variety of datasets.     

Transfer of learning in virtual environments: a new challenge?

The aim of all education is to apply what we learn in different contexts and to recognise and extend this learning to new situations. Virtual learning environments can be used to build skills. Recent research in cognitive psychology and education has shown that acquisitions are linked to the initial context. This provides a challenge for virtual reality in education or training. A brief overview of transfer issues highlights five main ideas: (1) the type of transfer enables the virtual environment (VE) to be classified according to what is learned; (2) the transfer process can create conditions within the VE to facilitate transfer of learning; (3) specific features of VR must match and comply with transfer of learning; (4) transfer can be used to assess a VE’s effectiveness; and (5) future research on transfer of learning must examine the singular context of learning. This paper discusses how new perspectives in cognitive psychology influence and promote transfer of learning through the use of VEs.     

Profiling sympathetic arousal in a physics course: How active are students?

Low arousal states (especially boredom) have been shown to be more deleterious to learning than high arousal states, though the latter have received much more attention (e.g., test anxiety, confusion, and frustration). Aiming at profiling arousal in the classroom (how active students are) and examining how activation levels relate to achievement, we studied sympathetic arousal during two runs of an elective advanced physics course in a real classroom setting, including the course exam. Participants were high school students (N = 24) who were randomly selected from the course population. Arousal was indexed from electrodermal activity, measured unobtrusively via the Empatica E4 wristband. Low arousal was the level with the highest incidence (60% of the lesson on average) and longest persistence, lasting on average three times longer than medium arousal and two times longer than high arousal level occurrences. During the course exam, arousal was positively and highly correlated (r = .66) with achievement as measured by the students' grades. Implications for a need to focus more on addressing low arousal states in learning are discussed, together with potential applications for biofeedback, teacher intervention, and instructional design.     

SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation

International Journal of Computer Vision - The use of human-level semantic information to aid robotic tasks has recently become an important area for both Computer Vision and Robotics. This has...