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Researchers commonly utilize coding‐based analysis of classroom asynchronous discussion contributions as part of studies of online learning and instruction. However, this analysis is inconsistent from study to study with over 50 coding schemes and procedures applied in the last eight years. The aim of this article is to provide a basis for more consistent use of coding schemes and to facilitate comparison of studies utilizing different coding schemes.

The paper identifies coding schemes presented in the research literature, classifies these schemes, and presents a list of synthesis codes reflecting the content of the many different schemes for each classification.

Based on the initial and follow‐up literature review, 56 different coding schemes were identified as having been employed within the last eight years. Initial sorting indicated that schemes primarily focused on identifying critical thinking, describing social interactions, or characterizing online discussion.

In addition to offering a comprehensive resource reflecting the coding schemes currently applied to the analysis of online, asynchronous discussion, the meta‐analysis results also inform regarding the current state of research in this area. In addition, current research trends and areas for potential new research and development are revealed.

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.

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.

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.

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.

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.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis.

To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency.

Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency.

This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches.

For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines.

To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

Standard Industrial Classification (SIC) codes are a type of controlled vocabulary used by business searchers when searching for corporate and industry data. Two four‐digit versions of the SIC coding scheme are used on business databases available through Dialog. Some Dialog database producers have opted to create a variation of the codes for use in their products. With the impending eight‐digit SIC coding scheme from Dun and Bradstreet, it will become increasingly difficult for the Dialog searcher to know which version of the codes to use when searching a particular database. A survey of the use of SIC codes in business databases on Dialog was undertaken. This paper presents the findings of this survey.

The purpose of this paper is to develop an ontological model of failure knowledge of automobile systems that will enhance the knowledge management of automobile system failures, which will help for design and maintenance of automobiles. Failure knowledge of automobile systems and components gained through maintenance and repair can mitigate future failures, if integrated in the design. This is an outcome of this paper.

A failure coding scheme is developed for assimilating various entities of automobile failure knowledge and an ontological model is developed for its systematic structuring and representation. The developed failure code is a combination of alphanumeric and numeric code that incorporates ingredients of the failure knowledge, which will help database management, with reduced data entry time and storage space.

The maintenance of automobiles not only brings back the systems into operating conditions but also convey a lot of information regarding the failures. This is a useful input to the designers in development of reliable and maintainable automobile systems. A knowledge base can be created for automobile systems/components failures from their maintenance and service experience.

Developed ontological model of automobile failure knowledge gained through maintenance experience can be shared across automobile manufacturers and service providers. This would help in design improvements, with ease and efficient undertaking of maintenance activities. This paper proposes the conceptual ontology structure, which is populated with three cases of automobile maintenance.

This research work is a first attempt to develop an ontological model for automobile failures from their maintenance and service experience. The novelty of the work is in its explicit consideration of all knowledge related to failures and maintenance of automobile systems, with their coding and structuring.

Social entrepreneurs create novel approaches to social problems such as poverty. But scaling these approaches to the dimension of the problem can be a difficult task. In the social enterprise sector, the subject of scaling has become a key dimension of organizational performance. This chapter advances the scholarly literature on the scaling of social enterprises, a literature which is currently in an embryonic stage and characterized by conceptual ambiguity and fragmented perspectives.

We engage realist philosophy of science to develop mechanism-based causal explanations of the scaling performance of social enterprises. We also develop a coding scheme to guide systematic empirical analysis and highlight the explanatory power of counterfactuals. Counterfactuals have been largely neglected in empirical research as they represent mechanisms that are enabled but remain unobservable – in a state of suppression or neutralization of their effects.

We question the ability of organizations to “socially engineer” desired outcomes and introduce a new construct – organizational closure competence. Anchored in realism, this construct provides a basis for productive approaches to social engineering. We elaborate on the importance of organizational closure competencies for scaling, derive a series of propositions, and develop ideas for future research and for practice.

Applying a realist lens allows us to add empirical rigor to research on social enterprises and scaling. Our approach constitutes a move from rich narratives to causal models and informs the way we design and evaluate efforts to address important societal challenges.

This chapter demonstrates how to operationalize realist philosophy of science for causal explanations of complex social phenomena and better utilize its theoretical and practical value.

The purpose of this study is to analyze the macro‐level discourse structure of literature reviews found in information science journal papers, and to identify different styles of literature review writing. Although there have been several studies of human abstracting, there are hardly any studies of how authors construct literature reviews.

This study is carried out in the context of a project to develop a summarization system to generate literature reviews automatically. A coding scheme was developed to annotate the high‐level organization of literature reviews, focusing on the types of information. Two sets of annotations were used to check inter‐coder reliability.

It was found that literature reviews are written in two distinctive styles, with different discourse structures. Descriptive literature reviews summarize individual papers/studies and provide more information on each study, such as research methods, results and interpretation. Integrative literature reviews provide fewer details of individual papers/studies, but focus on ideas and results extracted from these papers. They provide critical summaries of topics, and have a more complex structure of topics and sub‐topics. The reviewer's voice is also more dominant.

The coding scheme is useful for annotating the macro‐level discourse structure of literature reviews, and can be used for studying literature reviews in other fields. The basic characteristics of two styles of literature review writing are identified. The results have provided a foundation for further studies of literature reviews – to identify discourse relations and rhetorical functions employed in literature reviews, and their linguistic expressions.

Rudimentary programming is an essential, transferrable, problem solving skill in many higher education (HE) programmes in academic institutions including Software Engineering, Business Information Technology, Computer Games Development, Design and Technology. The purpose of this paper is to address some of the problematic issues associated with teaching programming by the utilisation of a new novel teaching approach called games-based construction learning (GBCL) to attempt to increase motivation, engagement and learning effectiveness. An international and national trend is to introduce coding at earlier education levels resulting in upper primary education (PE) being the focus of this paper to ascertain if GBCL using Scratch to teach programming concepts is more effective at different levels of upper PE.

A large-scale empirical study introducing GBCL to teach programming concepts into 16 classes between levels 4 and 7 in PE utilising 384 children. A detailed implementation framework for GBCL using Scratch in PE was utilised to address all incorporation issues and the games constructed by the children scored utilising a game codification scheme specifically designed to address programming and design as a quantification rubric. The experiment utilised eight 1- h lessons on GBCL using Scratch.

The resulted in 178 games of varying levels of complexity developed. The results indicated that GBCL was an effective mechanism to teach programming concepts using Scratch at all levels of upper PE. Primary seven students scored higher in relation to the design metric of the quantification codification rubric.

Under the Curriculum for Excellence (CfE) in Scotland non-traditional teaching approaches are encouraged and development of digital literacy skill is highly advocated. This has resulted in a new approach, novel approach called GBCL where children create their own games utilising an engine such as Scratch is gaining significant attention in terms of being a novel approach. Despite a plethora of similar studies associated with GBCL, it is still not as developed as games-based learning and requires further empirical studies to support the validity of the approach and resolve identified issues.

Computer programming itself can lead to a highly rewarding career in a number of sectors from games development to banking, such as cybersecurity and systems development. In the last decade, in particular due to the ubiquitous nature of technology there is an increasing international and national trend associated with teaching rudimentary programming concepts at a far younger age including secondary education and the upper PE level. Introducing programming at an earlier level is now being considered essential as the path to transfer from novice to expert programmer level in time is considered nearly a decade approximately. The introduction of GBCL interventions may yield positive results in a supplementary learning capacity in accordance with the CfE and increase the educational effectiveness of programming education in later levels of education.

This study presents a large-scale empirical evaluation of GBCL in upper PE utilising a compiled implementation framework for incorporation and a detailed game codification scheme to quantify the games produced highlighting coding constructs and design.