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  • × year_i:[2020 TO 2030}
  • × theme_ss:"Data Mining"
  1. Jones, K.M.L.; Rubel, A.; LeClere, E.: ¬A matter of trust : higher education institutions as information fiduciaries in an age of educational data mining and learning analytics (2020) 0.00 
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    Abstract
    Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of "learning analytics," this work can-and often does-surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1227-1241
  2. Wiegmann, S.: Hättest du die Titanic überlebt? : Eine kurze Einführung in das Data Mining mit freier Software (2023) 0.00 
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    Abstract
    Am 10. April 1912 ging Elisabeth Walton Allen an Bord der "Titanic", um ihr Hab und Gut nach England zu holen. Eines Nachts wurde sie von ihrer aufgelösten Tante geweckt, deren Kajüte unter Wasser stand. Wie steht es um Elisabeths Chancen und hätte man selbst das Unglück damals überlebt? Das Titanic-Orakel ist eine algorithmusbasierte App, die entsprechende Prognosen aufstellt und im Rahmen des Kurses "Data Science" am Department Information der HAW Hamburg entstanden ist. Dieser Beitrag zeigt Schritt für Schritt, wie die App unter Verwendung freier Software entwickelt wurde. Code und Daten werden zur Nachnutzung bereitgestellt.
  3. Organisciak, P.; Schmidt, B.M.; Downie, J.S.: Giving shape to large digital libraries through exploratory data analysis (2022) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.2, S.317-332
  4. Borgman, C.L.; Wofford, M.F.; Golshan, M.S.; Darch, P.T.: Collaborative qualitative research at scale : reflections on 20 years of acquiring global data and making data global (2021) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.6, S.667-682
  5. Goldberg, D.M.; Zaman, N.; Brahma, A.; Aloiso, M.: Are mortgage loan closing delay risks predictable? : A predictive analysis using text mining on discussion threads (2022) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.3, S.419-437

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