While there seems to be a “privacy paradox,” our discussion argues that faculty are making assumptions about existing privacy protections and making instructional choices that could harm students because their “risk calculus” is underinformed.
Data indicate that faculty believe privacy is important to intellectual behaviors and learning, but the discussion argues that faculty make choices that put students at risk. In the findings, we detail faculty perspectives of their privacy, students’ privacy, and the high degree to which they value both. The research herein addresses findings from a survey of over 500 full-time higher education instructors. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. The findings provide different insights on how these societal and human values are being considered in LA research, tools, applications and ethical frameworks. We present and highlight results of a literature review that was conducted across all the editions of the Learning Analytics & Knowledge (LAK) ACM conference proceedings. This research paper reviews these four values and their implications in algorithms and investigates their empirical existence in the interdisciplinary field of Learning Analytics (LA). As the use of intelligent algorithms and analytics are becoming more involved in how decisions are made in public and private life, the societal values of Fairness, Accountability and Transparency (FAT) and the multidimensional value of human Well-being are being discussed in the context of addressing potential negative and positive impacts of AI. P-curves suggest data provided evidential value and exploratory meta-regressions suggest that the addiction Stroop effect was not associated with design and analysis decisions.Ĭonclusions: The addiction Stroop effect is seemingly robust, however the adoption of consistent reporting guidelines is necessary to aid reliability and reproducibility.The scientific community is currently engaged in global efforts towards a movement that promotes positive human values in the ways we formulate and apply Artificial Intelligence (AI) solutions. Similarly, variability in analyses decisions would allow for 9,000 different methods for analyzing the Alcohol Stroop, 5,376 for the Smoking Stroop and 768 for the Drug Stroop. Many key design decisions were unreported. Results: Based on variability from previous research there are at least 1,451,520 different possible designs of the computerized Alcohol Stroop, 77,760 designs of the computerized Smoking Stroop and 112,640 for the Drug Stroop. For analysis decisions we extracted information on upper- and lower-bound reaction time cutoffs, removal of data based on standard error cutoffs, removal of participants based on overall performance, type of outcome used, and removal of errors. We extracted key information about the design of the Stroop tasks, including administration (paper-and-pencil vs. Method: Using a pre-registered design, we identified 95 studies utilizing an addiction Stroop (46 alcohol, 25 smoking, 24 drug-related). Our aim was to examine the variability in key design and analysis decisions of the addiction Stroop. This may in part be a consequence of substantial variability in methods used to operationalize attentional bias. Background: Theoretical models of addiction predict that an attentional bias toward substance-related cues plays a role in development and maintenance of addictive behaviors, although empirical data testing these predictions are somewhat equivocal.
0 kommentar(er)
