Search

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) technologies become more ubiquitous for streamlining and optimizing work, they are entering fields representing organizational logics at odds with the efficiency logic of automation. One such field is journalism, an industry defined by a logic enacted through professional norms, practices, and values. This paper examines the experience of technologists developing and employing natural language generation (NLG) in news organizations, looking at how they situate themselves and their technology in relation to newswork. Drawing on institutional logics, a theoretical framework from organizational theory, we show how technologists shape their logic for building these emerging technologies based on a theory of rationalizing news organizations, a frame of optimizing newswork, and a narrative of news organizations misinterpreting the technology. Our interviews reveal technologists mitigating tensions with journalistic logic and newswork by labeling stories generated by their systems as nonjournalistic content, seeing their technology as a solution for improving journalism, enabling newswork to move away from routine tasks. We also find that as technologists interact with news organizations, they assimilate elements from journalistic logic beneficial for benchmarking their technology for more lucrative industries. [ABSTRACT FROM AUTHOR]

Background: Different types of assessments influence learning and learning behaviour. Multiple‐choice questions (MCQs) reward partial knowledge and encourage surface learning, while open‐ended questions (OEQs) promote deeper learning. Currently, MCQs is part of team‐based learning (TBL) curriculum, and it is challenging to implement OEQs as immediate feedback is necessary. Objectives: We asked if MCQ and OEQs affect student achievement, student learning and student engagement differently in a TBL classroom. Methods: MCQs and OEQs test scores of N = 66 students were automatically captured in Learning Activity Management System (LAMS) and were compared using a switching replications quasi‐experimental design with pre‐ and post‐tests to answer the research questions. Student learning approaches and engagement in the team activities were assessed using the study process questionnaire and the structure of observed learning outcomes taxonomy respectively. Results and Conclusions: Students get significantly higher MCQ scores than OEQs for the same set of questions, but the reverse is true for application exercises (AEs), which focus on higher‐level application. Most students significantly deepened their learning approaches before OEQs, while poorly prepared students were less engaged during OEQ discussions. Interestingly students subjected to OEQs took less time and scored higher in AE discussions, suggesting better focus on higher‐level thinking. Implications: This project is significant as it bridges our understanding of the value of OEQs and TBL. Our approach is transferable to other courses, and thus it can improve the quality of teaching and learning in tertiary education. Lay Description: What is already known about this topic?: Multiple‐choice questions (MCQs) do not promote deep learning as much as open‐ended questions (OEQ) do.Team‐based learning (TBL) requires MCQs because of the need for immediate feedback.Automated marking can make it more efficient to provide immediate feedback on OEQs as required by the TBL approach. What this paper adds?: OEQs changed student‐learning approaches in the TBL context.OEQs changed the quality of team discussions during the team‐readiness assessment.Test (tRAT) and application exercise phases.Students were concerned about the validity of automated grading. Implications for practitioners: Consider using OEQs in TBL context for deepening student learning.Consider how to defuse students' scepticism of new technology.Consider forming teams with a mix of deep and surface learners. [ABSTRACT FROM AUTHOR]