TY - JOUR A1 - Herm, Lukas-Valentin A1 - Janiesch, Christian A1 - Fuchs, Patrick T1 - Der Einfluss von menschlichen Denkmustern auf künstliche Intelligenz – eine strukturierte Untersuchung von kognitiven Verzerrungen JF - HMD Praxis der Wirtschaftsinformatik N2 - Künstliche Intelligenz (KI) dringt vermehrt in sensible Bereiche des alltäglichen menschlichen Lebens ein. Es werden nicht mehr nur noch einfache Entscheidungen durch intelligente Systeme getroffen, sondern zunehmend auch komplexe Entscheidungen. So entscheiden z. B. intelligente Systeme, ob Bewerber in ein Unternehmen eingestellt werden sollen oder nicht. Oftmals kann die zugrundeliegende Entscheidungsfindung nur schwer nachvollzogen werden und ungerechtfertigte Entscheidungen können dadurch unerkannt bleiben, weshalb die Implementierung einer solchen KI auch häufig als sogenannte Blackbox bezeichnet wird. Folglich steigt die Bedrohung, durch unfaire und diskriminierende Entscheidungen einer KI benachteiligt behandelt zu werden. Resultieren diese Verzerrungen aus menschlichen Handlungen und Denkmustern spricht man von einer kognitiven Verzerrung oder einem kognitiven Bias. Aufgrund der Neuigkeit dieser Thematik ist jedoch bisher nicht ersichtlich, welche verschiedenen kognitiven Bias innerhalb eines KI-Projektes auftreten können. Ziel dieses Beitrages ist es, anhand einer strukturierten Literaturanalyse, eine gesamtheitliche Darstellung zu ermöglichen. Die gewonnenen Erkenntnisse werden anhand des in der Praxis weit verbreiten Cross-Industry Standard Process for Data Mining (CRISP-DM) Modell aufgearbeitet und klassifiziert. Diese Betrachtung zeigt, dass der menschliche Einfluss auf eine KI in jeder Entwicklungsphase des Modells gegeben ist und es daher wichtig ist „mensch-ähnlichen“ Bias in einer KI explizit zu untersuchen. N2 - Artificial intelligence (AI) is increasingly penetrating sensitive areas of everyday human life, resulting in the ability to support humans in complex and difficult tasks. The result is that intelligent systems are capable of handling not only simple but also complex tasks. For example, this includes deciding whether an applicant should be hired or not. Oftentimes, this decision-making can be difficult to comprehend, and consequently incorrect decisions may remain undetected, which is why these implementations are often referred to as a so-called black box. Consequently, there is the threat of unfair and discriminatory decisions by an intelligent system. If these distortions result from human actions and thought patterns, it is referred to as a cognitive bias. However, due to the novelty of this subject, it is not yet apparent which different cognitive biases can occur within an AI project. The aim of this paper is to provide a holistic view through a structured literature review. Our insights are processed and classified according to the Cross-Industry Standard Process for Data Mining (CRISP-DM) model, which is widely used in practice. This review reveals that human influence on an AI is present in every stage of the model’s development process and that “human-like” biases in an AI must be examined explicitly. T2 - The impact of human thinking on artificial intelligence – a structured investigation of cognitive biases KW - Menschliche Denkmuster KW - Maschinelles Lernen KW - Künstliche Intelligenz KW - Literaturanalyse KW - cognitive biases KW - machine learning KW - artificial intelligence KW - literature review Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323787 SN - 1436-3011 VL - 59 IS - 2 ER - TY - JOUR A1 - Just, Janina A1 - Siller, Hans-Stefan T1 - The role of mathematics in STEM secondary classrooms: a systematic literature review JF - Education Sciences N2 - Nowadays, science, technology, engineering, and mathematics (STEM) play a critical role in a nation’s global competitiveness and prosperity. Thus, there is a need to educate students in these subjects to meet the current and future demands of personal life and society. While applications, especially in science, engineering, and technology, are directly obvious, mathematics underpins the other STEM disciplines. It is recognized that mathematics is the foundation for all other STEM disciplines; the role of mathematics in classrooms is not clear yet. Therefore, the question arises: What is the current role of mathematics in secondary STEM classrooms? To answer this question, we conducted a systematic literature review based on three publication databases (Web of Science, ERIC, and EBSCO Teacher Referral Center). This literature review paper is intended to contribute to the current state of the role of mathematics in STEM education in secondary classrooms. Through the search, starting with 1910 documents, only 14 eligible documents were found. In these, mathematics is often seen as a minor matter and a means to an end in the eyes of science educators. From this, we conclude that the role of mathematics in the STEM classroom should be further strengthened. Overall, the paper highlights a major research gap, and proposes possible initial solutions to close it. KW - STEM education KW - role of mathematics in STEM KW - literature review KW - STEM integration KW - STEM classroom KW - secondary education Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288075 SN - 2227-7102 VL - 12 IS - 9 ER - TY - JOUR A1 - Schwab, Frank A1 - Hennighausen, Christine A1 - Adler, Dorothea C. A1 - Carolus, Astrid T1 - Television Is Still “Easy” and Print Is Still “Tough”? More Than 30 Years of Research on the Amount of Invested Mental Effort JF - Frontiers in Psychology N2 - We provide a literature overview of 30 years of research on the amount of invested mental effort (AIME, Salomon, 1984), illuminating relevant literature in this field. Since the introduction of AIME, this concept appears to have vanished. To obtain a clearer picture of where the theory of AIME has diffused, we conducted a literature search focusing on the period 1985–2015. We examined scientific articles (N = 244) that cite Salomon (1984) and content-analyzed their keywords. Based on these keywords, we identified seven content clusters: affect and motivation, application fields, cognition and learning, education and teaching, media technology, learning with media technology, and methods. We present selected works of each content cluster and describe in which research field the articles had been published. Results indicate that AIME was most commonly (but not exclusively) referred to in the area of educational psychology indicating its importance regarding learning and education, thereby investigating print and TV, as well as new media. From a methodological perspective, research applied various research methods (e.g., longitudinal studies, experimental designs, theoretical analysis) and samples (e.g., children, college students, low income families). From these findings, the importance of AIME for further research is discussed. KW - AIME KW - amount of invested mental effort KW - literature review KW - content-analysis KW - content cluster Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-189965 SN - 1664-1078 VL - 9 IS - 1098 ER -