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Article
Publication date: 14 May 2020

Di Wu, Lei Wu, Alexis Palmer, Dr Kinshuk and Peng Zhou

Interaction content is created during online learning interaction for the exchanged information to convey experience and share knowledge. Prior studies have mainly focused…

Abstract

Purpose

Interaction content is created during online learning interaction for the exchanged information to convey experience and share knowledge. Prior studies have mainly focused on the quantity of online learning interaction content (OLIC) from the perspective of types or frequency, resulting in a limited analysis of the quality of OLIC. Domain concepts as the highest form of interaction are shown as entities or things that are particularly relevant to the educational domain of an online course. The purpose of this paper is to explore a new method to evaluate the quality of OLIC using domain concepts.

Design/methodology/approach

This paper proposes a novel approach to automatically evaluate the quality of OLIC regarding relevance, completeness and usefulness. A sample of OLIC corpus is classified and evaluated based on domain concepts and textual features.

Findings

Experimental results show that random forest classifiers not only outperform logistic regression and support vector machines but also their performance is improved by considering the quality dimensions of relevance and completeness. In addition, domain concepts contribute to improving the performance of evaluating OLIC.

Research limitations/implications

This paper adopts a limited sample to train the classification models. It has great benefits in monitoring students’ knowledge performance, supporting teachers’ decision-making and even enhancing the efficiency of school management.

Originality/value

This study extends the research of domain concepts in quality evaluation, especially in the online learning domain. It also has great potential for other domains.

Details

The Electronic Library , vol. 38 no. 3
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 28 October 2014

Kyle Dillon Feuz and Diane J. Cook

The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications…

Abstract

Purpose

The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications require information about the activities currently being performed, but activity recognition algorithms typically require substantial amounts of labeled training data for each setting. One solution to this problem is to leverage transfer learning techniques to reuse available labeled data in new situations.

Design/methodology/approach

This paper introduces three novel heterogeneous transfer learning techniques that reverse the typical transfer model and map the target feature space to the source feature space and apply them to activity recognition in a smart apartment. This paper evaluates the techniques on data from 18 different smart apartments located in an assisted-care facility and compares the results against several baselines.

Findings

The three transfer learning techniques are all able to outperform the baseline comparisons in several situations. Furthermore, the techniques are successfully used in an ensemble approach to achieve even higher levels of accuracy.

Originality/value

The techniques in this paper represent a considerable step forward in heterogeneous transfer learning by removing the need to rely on instance – instance or feature – feature co-occurrence data.

Details

International Journal of Pervasive Computing and Communications, vol. 10 no. 4
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 5 November 2018

Réka Vas, Christian Weber and Dimitris Gkoumas

Connectivism has been proposed to explain the impact of new technologies on learning. According to this approach, learning may occur even outside the individual within an…

Abstract

Purpose

Connectivism has been proposed to explain the impact of new technologies on learning. According to this approach, learning may occur even outside the individual within an organization or a system. Learning objectives are not defined in advance and learning requires the ability to form connections and use networks to find the required knowledge. The connections by which individuals can learn are more important than what they currently know. The purpose of this paper is to investigate if a measure, rating the importance of concepts, can be derived from a network representation of the learning domain and if highly connected concepts – with high importance value – can describe whether information is explored in such ways as assumed by connectivism.

Design/methodology/approach

The authors empirically examined if the proposed measure can provide insight on the role of connections in learning and explain the reasons behind passing certain parts of a test using a linear regression model.

Findings

The results are twofold. First, an implementation of the information exploration principle of connectivism has been introduced, applying semantic technologies and the importance measure. Second, although no significant effects could be isolated, trends in performance improvement concerning highly important concepts were identified.

Originality/value

However, connectivism has been known since 2005, it is still lacking for successful implementations. The presented approach of a concept importance measure is a promising starting point by providing means of connected learning, enabling individuals to effectively improve their personal abilities to better fit job demand.

Details

International Journal of Manpower, vol. 39 no. 8
Type: Research Article
ISSN: 0143-7720

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Article
Publication date: 25 February 2014

Lene Bjerg Hall-Andersen and Ole Broberg

The purpose of this paper is to shed light on the problematics of learning across knowledge boundaries in organizational settings. The paper specifically explores learning

Abstract

Purpose

The purpose of this paper is to shed light on the problematics of learning across knowledge boundaries in organizational settings. The paper specifically explores learning processes that emerge, when a new knowledge domain is introduced into an existing organizational practice with the aim of creating a new combined practice.

Design/methodology/approach

A case study was carried out as a “natural experiment” in an engineering consultancy, where emerging initiatives to integrate the newly acquired competencies into the existing practice were explored. A theoretical framework informed by selected perspectives on learning processes and boundary processes was applied on three illustrative vignettes to illuminate learning potentials and shortcomings in boundary processes.

Findings

In the engineering consultancy, it was found that while learning did occur in the consultancy organization, it remained discrete in ‘pockets’ of learning; mainly at an individual level, at project level or as domain-specific learning. Learning processes were intertwined with elements of domain-specific interests, power, managerial support, structural conditions, material and epistemic differences between knowledge domains.

Research limitations/implications

The finding in this paper is based on a single case study: hence, the findings' generalizability may be limited.

Practical implications

The paper argues that learning across knowledge domains needs various forms of supporting initiatives and constant readiness to alter or counteract when an initiative's shortcomings appear or undesired learning loops arise.

Originality/value

The paper contributes to understanding the complexity of learning across knowledge boundaries in organizational settings.

Details

Journal of Workplace Learning, vol. 26 no. 2
Type: Research Article
ISSN: 1366-5626

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Book part
Publication date: 28 September 2011

Lesley Scopes

Our university demonstrates a strong investment in online education and as part of continuing development delivers some existing online programs in a 3D virtual world…

Abstract

Our university demonstrates a strong investment in online education and as part of continuing development delivers some existing online programs in a 3D virtual world. Faculty members need a plan to engage, so they were guided in the adoption of our cybergogy of learning archetypes and learning domains to draw together various aspects of learning. Together we weave threads from orthodox theories with a doctrine of educational technologies that encompasses social-centric 3D interactive virtual environments. This chapter documents the growth of the model from theory into practice to provide a framework for instructors to plan their virtual courses. Five Second Life®-enhanced courses were developed, scheduled and marketed to enrolled students to test the framework. The teaching and learning strategies adopted are reported and outcomes are presented.

Details

Transforming Virtual World Learning
Type: Book
ISBN: 978-1-78052-053-7

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Article
Publication date: 27 October 2020

Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu

The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue…

Abstract

Purpose

The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.

Design/methodology/approach

In this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.

Findings

The experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.

Originality/value

Experiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

Details

Data Technologies and Applications, vol. 55 no. 1
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 12 December 2019

Yuxin Chen, Christopher D. Andrews, Cindy E. Hmelo-Silver and Cynthia D'Angelo

Computer-supported collaborative learning (CSCL) is widely used in different levels of education across disciplines and domains. Researchers in the field have proposed…

Abstract

Purpose

Computer-supported collaborative learning (CSCL) is widely used in different levels of education across disciplines and domains. Researchers in the field have proposed various conceptual frameworks toward a comprehensive understanding of CSCL. However, as the definition of CSCL is varied and contextualized, it is critical to develop a shared understanding of collaboration and common definitions for the metrics that are used. The purpose of this research is to present a synthesis that focuses explicitly on the types and features of coding schemes that are used as analytic tools for CSCL.

Design/methodology/approach

This research collected coding schemes from researchers with diverse backgrounds who participated in a series of workshops on collaborative learning and adaptive support in CSCL, as well as coding schemes from recent volumes of the International Journal of Computer-Supported Collaborative learning (ijCSCL). Each original coding scheme was reviewed to generate an empirically grounded framework that reflects collaborative learning models.

Findings

The analysis generated 13 categories, which were further classified into three domains: cognitive, social and integrated. Most coding schemes contained categories in the cognitive and integrated domains.

Practical implications

This synthesized coding scheme could be used as a toolkit for researchers to pay attention to the multiple and complex dimensions of collaborative learning and for developing a shared language of collaborative learning.

Originality/value

By analyzing a set of coding schemes, the authors highlight what CSCL researchers find important by making these implicit understandings of collaborative learning visible and by proposing a common language for researchers across disciplines to communicate by referencing a synthesized framework.

Details

Information and Learning Sciences, vol. 121 no. 1/2
Type: Research Article
ISSN: 2398-5348

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Article
Publication date: 31 August 2010

Dirk Börner, Christian Glahn, Slavi Stoyanov, Marco Kalz and Marcus Specht

The present paper introduces concept mapping as a structured participative conceptualization approach to identify clusters of ideas and opinions generated by experts…

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Abstract

Purpose

The present paper introduces concept mapping as a structured participative conceptualization approach to identify clusters of ideas and opinions generated by experts within the domain of mobile learning. Utilizing this approach, the paper aims to contribute to a definition of key domain characteristics by identifying the main educational concepts related to mobile learning.

Design/methodology/approach

A short literature review points out the attempts to find a clear definition for mobile learning as well as the different perspectives taken. Based on this an explorative case study was conducted, focusing on the educational problems that underpin the expectations on mobile learning. Using the concept mapping approach the study identified these educational problems and the related domain concepts. The respective results were then analyzed and discussed.

Findings

The chosen approach produced several means to interpret the experts' ideas and opinions, such as a cluster map illustrating and structuring substantial accordances. These means help to gain new insights on the emphasis and relation of the core educational concepts of mobile learning. The core educational concepts of mobile learning identified are: “access to learning”, “contextual learning”, “orchestrating learning across contexts”, “personalization”, and “collaboration”.

Originality/value

The paper is original as it uses a unique conceptualization approach to work out the educational problems that can be addressed by mobile learning and thus contributes to a domain definition based on identified issues, featured concepts, and derived challenges. In contrast to existing approaches for defining mobile learning, the present approach relies completely on the expertise of domain experts.

Details

Campus-Wide Information Systems, vol. 27 no. 4
Type: Research Article
ISSN: 1065-0741

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Article
Publication date: 6 March 2018

Bilal Abu-Salih, Pornpit Wongthongtham and Chan Yan Kit

This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a…

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1113

Abstract

Purpose

This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs.

Design/methodology/approach

This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets.

Findings

Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics.

Research limitations/implications

This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data.

Practical implications

This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc.

Originality/value

This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

Details

Journal of Knowledge Management, vol. 22 no. 5
Type: Research Article
ISSN: 1367-3270

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Article
Publication date: 7 March 2016

Veronika Leicher and Regina H. Mulder

The purpose of this replication study is to identify relevant individual and contextual factors influencing learning from errors at work and to determine if the predictors…

Abstract

Purpose

The purpose of this replication study is to identify relevant individual and contextual factors influencing learning from errors at work and to determine if the predictors for learning activities are the same for the domains of nursing and retail banking.

Design/methodology/approach

A cross-sectional replication study was carried out in retail banking departments of a German bank. In a pre-study, interviews were conducted with experts (N = 4) of retail banking. The pre-study was necessary to develop vignettes describing authentic examples of error situations which were part of the questionnaire. The questionnaire was filled out by 178 employees.

Findings

Results indicate that the estimation of an error as relevant for learning positively predicts bankers’ engagement in social learning activities. The tendency to cover up an error predicts bankers’ engagement negatively. There are also indirect effects of error strain and the perception of a safe social team climate on the engagement in social learning.

Originality/value

This paper contributes to the generalization of results by transferring and testing a model of learning from errors in a domain different from the previous domains where this topic was investigated.

Details

Journal of Workplace Learning, vol. 28 no. 2
Type: Research Article
ISSN: 1366-5626

Keywords

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