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Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate knowledge transfer, reuse of best practices, and minimize knowledge requirements. On the other hand, to unlock the potential of ever-growing textual data such as publications, patents, social media data, and documents of various forms, digital innovation is increasingly needed. Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. The processing of unstructured textual data to generate new and useful ideas is referred to as idea mining. Existing literature about idea mining merely overlooks the utilization of standard data mining process models. Therefore, the purpose of this paper is to propose a reusable model to generate ideas, CRISP-DM, for Idea Mining (CRISP-IM). The design and development of the CRISP-IM are done following the design science approach. The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. The adapted CRISP-IM can be used to guide the process of identifying trends using scholarly literature datasets or temporally organized patent or any other textual dataset of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left for future study.

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Self-driving is an emerging technology which has several benefits such as improved quality of life, crash reductions, and fuel efficiency. There are however concerns regarding the utilization of self-driving technology such as affordability, safety, control, and liabilities. There is an increased effort in research centers, academia, and the industry to advance every sphere of science and technology yet it is getting harder to find innovative ideas. However, there is untapped potential to analyze the increasing research results using visual analytics, scientometrics, and machine learning. In this paper, we used scientific literature database, Scopus to collect relevant dataset and applied a visual analytics tool, CiteSpace, to conduct co-citation clustering, term burst detection, time series analysis to identify emerging trends, and analysis of global impacts and collaboration. Also, we applied unsupervised topic modeling, Latent Dirichlet Allocation (LDA) to identify hidden topics for gaining more insight about topics regarding self-driving technology. The results show emerging trends relevant to self-driving technology and global and regional collaboration between countries. Moreover, the result form the LDA shows that standard topic modeling reveals hidden topics without trend information. We believe that the result of this study indicates key technological areas and research domains which are the hot spots of the technology. For the future, we plan to include dynamic topic modeling to identify trends.

PurposeTo improve the academic integrity of online examinations, digital proctoring systems have recently been implemented in higher education institutions (HEIs). The paper aims to understand how digital proctoring has been practised in higher education (HE) and proposes future research directions for studying digital proctoring in HE.Design/methodology/approachA systematic literature review was conducted. The PRISMA procedure was adapted for the literature search. The topics were identified by topic modelling techniques from 154 relevant publications in seven databases.FindingsSeven widely discussed topics in literature were identified, including solutions for detecting cheating and student authentication, challenges/issues of uptakes and students' performance in different proctoring environments.Research limitations/implicationsThis paper provides insights for academics, policymakers, practitioners and students to understand the implementation of digital proctoring in academia, its adoption by HEIs, impacts on students' and educators' performance and the rapid increase in its use for digital exams in HEIs, with particular emphasis on the impacts of the systems on digitalising examinations in HE.Originality/valueThis review paper has systematically and critically described the state-of-the-art literature on digital proctoring in HE and provides useful insights and implications for future research on digital proctoring, and how academic integrity in online examinations can be enhanced, along with digitalising HE.