scholarly journals The cone-of-learning: a visual comparison of learning systems

2016 ◽  
Vol 28 (1) ◽  
pp. 21-39
Author(s):  
John Hamilton ◽  
Singwhat Tee
Keyword(s):  
Deep Learning ◽  
Student Learning ◽  
Student Perceptions ◽  
Learning Experiences ◽  
Learning Systems ◽  
Learning System ◽  
Learning Approaches ◽  
Flexible Learning ◽  
Content Type ◽  
Tertiary Institutions

Purpose – Four learning modes, interacting through students as different learning systems, are mapped into a cone-of-learning continuum that allows tertiary institutions to visually re-consider where within their cone-of-learning, they choose to position their learning approaches. Two forms of blended learning are also distinguished. The paper aims to discuss these issues. Design/methodology/approach – Undergraduate law, business, IT, and creative arts student perceptions are structural equation modelled (SEM) into traditional, blended-enabled, blended-enhanced, and flexible learning systems. Findings – Within the SEM derived learning cone-of-learning continuum, a migration from traditional learning systems towards blended and flexible learning systems typically offers higher-net levels of undergraduate student learning experiences and outcomes. Research limitations/implications – The authors do not capture learning system feedback loops, but the cone-of-learning approaches can position against chosen competitors. The authors recognise benchmark, positioning, and transferability differences may exist between different tertiary institutions; different learning areas; and different countries of operation. Cone-of-learning studies can expand to capture student perceptions of their value acquisitions, overall satisfaction, plus trust, and loyalty considerations. Practical implications – The cone-of-learning shows shifts towards flexibility as generating higher student learning experiences, higher student learning outcomes, and as flexible technologies mature this demands higher student inputs. These interactive experiential systems approaches can readily incorporate new technologies, gamifications, and engagements which are testable for additional student deep-learning contributions. Experiential deep-learning systems also have wide industrial applications. Social implications – Understanding the continuum of transitioning between and across deeper-learning systems offers general social benefit. Originality/value – Learning system studies remain complex, variable systems, dependent on instructors, students, and their shared experiential engagements environments. This cone-of-learning continuum approach is useful for educators, business, and societal life-long learners who seek to gauge learning and outcomes.

Student Learning in an Online Environment

2011 ◽  
pp. 224-234
Author(s):  
Rodney Arambewela
Keyword(s):  
Deep Learning ◽  
Student Learning ◽  
Learning Experience ◽  
Learning System ◽  
Learning Approaches ◽  
Learning Contexts ◽  
Technology In Teaching ◽  
E Learning ◽  
Learning Orientations ◽  
Surface Learning

The increasing class sizes, changing expectations, diversity and mobility of students and the use of computer technology in teaching have challenged universities, world over, to review educational courses and delivery to provide a more satisfying learning experience to students. Understanding how students learn is essential in this process and continuous enquiry into teaching practices for their effectiveness towards enhancement of student learning outcomes is therefore considered a vital strategy. This chapter discusses an exploratory study on the differences in the learning approaches of a group of students in a second year marketing course in an Australian university. E-learning system remains the primary communication and the learning resource of these students. Results indicate that there are no significant differences in the study approaches of students but on average they seem to demonstrate deep learning than surface learning although they may differ in terms of the learning contexts. The study also reveals that in comparison female students and older aged students seem to demonstrate deep learning orientations than surface learning orientations.

Immersive spatial narratives as a framework for augmenting creativity in foresight-based learning systems

2019 ◽  
Vol 27 (2) ◽  
pp. 57-71
Author(s):  
Derek Woodgate
Keyword(s):  
Multimedia Learning ◽  
Cognitive Learning ◽  
Learning Systems ◽  
Learning System ◽  
Learning Approaches ◽  
Learning Tools ◽  
Content Type ◽  
The Future ◽  
Enhanced Learning ◽  
Future Landscape

Purpose The ability to project oneself into a future landscape is a critical aspect for studying and practicing the science of foresight and foresight-based learning systems. The purpose of this paper is to discuss how we can construct immersive spatial narratives through multimedia-enhanced learning approaches, to increase deeper learner immersion and levels of creativity to transport the learner into a simulated 2030 landscape by reducing the distance between the projected reality and the Self. Design/methodology/approach The author designed a foresight-based course on the Future of Mobile Learning underpinned by a new learning system that embraced the concept of immersive spatial narratives, combining physical, virtual and cognitive learning spaces, which enable students to explore complex, undiscovered or unstructured knowledge. Practicing was carried out on 35 students who had completed the course during the preceding three years through a questionnaire and interviews to establish increased levels of creativity in a simulated future landscape. Findings The paper established that the addition of multimedia learning environments and tools to foresight-based learning creates immersive spatial narratives that increase creativity and learner ability to project him/herself into a simulated future landscape. In all, 75 per cent of the respondents stated that having to think about the future and place themselves in a practicing landscape increased their creative skills. Originality/value A new, foresight-based learning system driven by the concept of immersive spatial narratives, enhanced with student-created multimedia learning tools. The system demonstrated how this approach helps to increase learner creativity and the ability to transition from their Present Self to their Future Self.

scholarly journals A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2514
Author(s):  
Tharindu Kaluarachchi ◽  
Andrew Reis ◽  
Suranga Nanayakkara
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Review Paper ◽  
Learning Systems ◽  
Learning Approaches ◽  
Application Development ◽  
Research Gaps ◽  
Domain Experts ◽  
Working Definition ◽  
Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

Assessing the engagement rates and satisfaction levels of various clinical health science student sub-groups using supplementary eLearning resources in an introductory anatomy and physiology unit

2019 ◽  
Vol 119 (1) ◽  
pp. 2-17 ◽  
Author(s):  
Cherylea J. Browne
Keyword(s):  
Frequency Analysis ◽  
Learning Experiences ◽  
Health Science ◽  
Learning Approaches ◽  
Core Knowledge ◽  
Learning Engagement ◽  
Content Type ◽  
Anatomy And Physiology ◽  
Podiatry Students ◽  
Support Students

Purpose Introductory anatomy and physiology provide a core knowledge base to students within clinical health science courses. Increased student numbers, as well as reduced access to laboratory-based cadaveric resources, have created a need for enhanced learning approaches to support learning. The streamlining of courses has also resulted in the need to effectively engage course sub-groups within large units. The purpose of this paper is to utilize the eLearning activities to investigate engagement and satisfaction levels within students undertaking an anatomy and physiology unit. Design/methodology/approach A total of 19 formative quizzes were made available to students. Online practical anatomy laboratories covered anatomical content, and physiology quizzes covered physiological content. Student engagement was compared using frequency analysis across students studying varying courses. Satisfaction was determined by analyzing student’s feedback using frequency analysis. Findings Students accessed the learning activities 29,898 times over semester, with the peak access (37 percent) prior to the closed book exams. The resources were utilized primarily as an exam preparation tool rather than consistently throughout semester. Out of the various courses, the Paramedicine, Physiotherapy and Podiatry students were the most engaged, with the highest percent of “engaged/highly engaged” students. Students from various courses shared very similar views of the perceived benefit of the eLearning activities. Practical implications These results indicated a difference in engagement levels between the students of various course sub-groups, and therefore suggests that the development of course-specific eLearning activities is necessary in large, streamlined units to achieve a more focused approach to support students’ learning, engagement and success, so that positive and beneficial learning experiences are ensured for all students. Originality/value These results suggest that in the future, development of eLearning activities is necessary to achieve a more focused approach to support students’ learning, engagement and success, so that positive and beneficial learning experiences are ensured for all.

scholarly journals Machine learning for human learners: opportunities, issues, tensions and threats

2020 ◽  
Author(s):  
Mary E. Webb ◽  
Andrew Fluck ◽  
Johannes Magenheim ◽  
Joyce Malyn-Smith ◽  
Juliet Waters ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ethical Issues ◽  
Practical Experience ◽  
Learning Systems ◽  
Learning System ◽  
Adaptive Behaviour ◽  
Recent Developments ◽  
Key Aspects ◽  
School Curricula

AbstractMachine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.

scholarly journals Building Trust in Deep Learning System towards Automated Disease Detection

2019 ◽  
Vol 33 ◽  
pp. 9516-9521 ◽  
Author(s):  
Zhan Wei Lim ◽  
Mong Li Lee ◽  
Wynne Hsu ◽  
Tien Yin Wong
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Medical Images ◽  
Real Life ◽  
High Accuracy ◽  
Learning Systems ◽  
Learning System ◽  
Disease Detection ◽  
Building Trust ◽  
Uncertainty Estimates

Though deep learning systems have achieved high accuracy in detecting diseases from medical images, few such systems have been deployed in highly automated disease screening settings due to lack of trust in how well these systems can generalize to out-of-datasets. We propose to use uncertainty estimates of the deep learning system’s prediction to know when to accept or to disregard its prediction. We evaluate the effectiveness of using such estimates in a real-life application for the screening of diabetic retinopathy. We also generate visual explanation of the deep learning system to convey the pixels in the image that influences its decision. Together, these reveal the deep learning system’s competency and limits to the human, and in turn the human can know when to trust the deep learning system.

Comparing perceived learning experiences of two concurrent cohorts under curriculum reform in Hong Kong

2017 ◽  
Vol 25 (3) ◽  
pp. 270-286
Author(s):  
Yue Zhao ◽  
Jenny M.Y. Huen ◽  
Michael Prosser
Keyword(s):  
Hong Kong ◽  
Student Learning ◽  
Curriculum Reform ◽  
Learning Experiences ◽  
Perceived Learning ◽  
Content Type ◽  
Latent Mean Differences ◽  
Mean Differences ◽  
The Impact ◽  
Student Learning Experiences

Purpose Hong Kong has undergone extensive curriculum reform and shifted from a three-year to a four-year university system. With a nuanced look at the impact of the curriculum reform, the purpose of the present study was to compare two concurrent cohorts by examining the extent to which the students in each cohort perceived their learning environment and learning outcomes differently and to what extent their perceptions differed. Design/methodology/approach A sample of 3,102 first-year students enrolled at a Hong Kong university responded to the student learning experience questionnaire (SLEQ). The perceived learning experiences between the two cohorts were tapped through several latent factors and validated through tests of measurement invariance. Latent mean differences were then compared. A multiple-group confirmatory factor analysis approach was used. Findings The findings suggested that measurement invariance of the scales of the SLEQ was established and that the two cohorts of students perceived largely similar learning experiences. The latent mean differences were statistically significant on the scales of feedback from teacher, clear goals and standards, personal integrity and ethics, intercultural competence, global perspective and civic commitment. Practical implications The results facilitate the understanding of perceived student learning experiences with evidence-based implications for enhancing student learning experiences and refining the four-year curriculum under the new curriculum reform conditions. Originality/value This is the first institution-wide study that compares student learning experiences of two concurrent cohorts under curriculum reform initiatives of Hong Kong. It provides a meaningful and pertinent example for educators and researchers worldwide to analyze the impact of curriculum reform from the perspective of students’ perceived learning experiences. Similar studies adopting rigorous approaches in the measurement of students’ perceived learning experiences are relatively rare in higher education. Such efforts are encouraged for accountability and quality improvement purposes.

CID: Categorical Influencer Detection on microtext-based social media

2020 ◽  
Vol 44 (5) ◽  
pp. 1027-1055
Author(s):  
Thanh-Tho Quan ◽  
Duc-Trung Mai ◽  
Thanh-Duy Tran
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Learning Approaches ◽  
Content Type ◽  
Modeling Process ◽  
Variational Autoencoder ◽  
Media Channels

PurposeThis paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.Design/methodology/approachWe deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.FindingsThe approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.Research limitations/implicationsThis work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.Practical implicationsThis work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.Originality/valueIn this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

A sustainable quality assessment model for the information delivery in E-learning systems

2018 ◽  
Vol 46 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Shahid Farid ◽  
Rodina Ahmad ◽  
Mujahid Alam ◽  
Atif Akbar ◽  
Victor Chang
Keyword(s):  
Quality Assessment ◽  
State Of The Art ◽  
Statistical Tests ◽  
Information Delivery ◽  
Learning Systems ◽  
Learning System ◽  
Assessment Model ◽  
Content Type ◽  
E Learning

Purpose The purpose of this study is to propose a sustainable quality assessment approach (model) for the e-learning systems keeping software perspective under consideration. E-learning is becoming mainstream due to its accessibility, state-of-the-art learning, training ease and cost effectiveness. However, the poor quality of e-learning systems is one of the major causes of several failures reported. Moreover, this arena lacks well-defined quality assessment measures. Hence, it is quite difficult to measure the overall quality of an e-learning system effectively. Design/methodology/approach A pragmatic mixed-model philosophy was adopted for this study. A systematic literature review was performed to identify existing e-learning quality models and frameworks. Semi-structured interviews were conducted with e-learning experts following empirical investigations to identify the crucial quality characteristics of e-learning systems. Various statistical tests like principal component analysis, logistic regression, chi-square and analysis of means were applied to analyze the empirical data. These led to an adequate set of quality indicators that can be used by higher education institutions to assure the quality of e-learning systems. Findings A sustainable quality assessment model for the information delivery in e-learning systems in software perspective has been proposed by exploring the state-of-the-art quality assessment/evaluation models and frameworks proposed for the e-learning systems. The proposed model can be used to assess and improve the process of information discovery and delivery of e-learning. Originality/value The results obtained led to conclude that very limited attention is given to the quality of e-learning tools despite the importance of quality and its effect on e-learning system adoption and promotion. Moreover, the identified models and frameworks do not adequately address quality of e-learning systems from a software perspective.

An m-learning framework for ERP systems in higher education

2014 ◽  
Vol 11 (4) ◽  
pp. 287-301 ◽  
Author(s):  
Brenda Scholtz ◽  
Mando Kapeso
Keyword(s):  
Higher Education ◽  
Success Factors ◽  
Theoretical Framework ◽  
Learning Systems ◽  
Learning System ◽  
Erp Systems ◽  
Content Type ◽  
Erp System ◽  
E Learning ◽  
Learning Projects

Purpose – The purpose of this paper is to investigate the factors of m-learning approaches which can be used for enterprise resource planning (ERP) system training and to propose a theoretical framework for m-learning of ERP systems. Design/methodology/approach – A literature review of several theories relating to success factors for mobile learning (m-learning) and electronic learning (e-learning) are analysed and a theoretical framework of success factors for m-learning of ERP systems is proposed. Two field studies are undertaken to identify the features of e-learning and m-learning systems which users enjoyed and which related to the factors identified in the theoretical framework. The technology acceptance model (TAM) was used to evaluate the acceptance, usefulness and perceived ease of use (PEOU) of the two systems evaluated in the field study, the openSAP e-learning application and the SAP Learn Now m-learning application. Findings – The results confirmed several of the theoretical elements identified in the framework and the m-learning system was rated positively for PEOU and perceived usefulness (PU). The findings confirmed other studies showing the importance of the quality of course content in e-learning and m-learning projects. Research limitations/implications – The empirical study was limited to a small number of participants in higher education. However, a deeper understanding of the factors influencing m-learning for ERP systems was obtained. Practical implications – The study provides a valuable practical contribution because the framework can be used in the improved design of an ERP m-learning approach, which in turn can lead to an improvement in ERP training and education programmes and ultimately ERP project success. Originality/value – Several studies propose the use of m-learning systems. However, research related to the factors impacting on m-learning projects for ERP system training is limited. The paper presents original work and the results provide a valuable contribution to several theories of m-learning.

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