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Institute
As meta-analyses demonstrate feedback effects on performance, our study examined possible mediators. Based on our cognitive-motivational model [Vollmeyer, R., & Rhemberg, F. (1998). Motivationale Einflusse auf Erwerb und Anwendung von Wissen in einem computersimulierten System [Motivational influences on the acquisition and application of knowledge in a simulated system]. Zeitschrift fur Padagogische Psychologie, 12, 11-23] we examined how feedback changed (1) strategies, and (2) motivation during learning, and by doing so improved (3) final performance. Students (N = 211) learned how a dynamic system works and how to reach given goal states for the system. One group received feedback (i.e., knowledge of performance) the other one did not. We expected learners to improve after they received the first feedback. However, we found that learners expecting feedback used better strategies right from the start. Thus, they acquired more knowledge over fewer trials. Although we had also expected effects of feedback on motivation during learning, we could not support this hypothesis. (c) 2005 Elsevier Ltd. All rights reserved
Achievement motive imagery in German schoolbooks : a pilot study testing McClelland's hypothesis
(2009)
McClelland [McClelland, D.C. (1961). The achieving society. Princeton, NJ: Van Nostrand] observed that the amount of achievement imagery in children's books predicted the economic development of societies. He argued that achievement imagery is an indicator of a motivational climate, and when children grow up in a society that emphasizes the striving for achievement, they will be more economically productive later on. We tested McClelland's hypothesis by coding school textbooks for achievement imagery from two German federal states (Baden-Wurttemberg and Bremen) with pronounced differences in economic and educational conditions. As expected, the schoolbooks from the state with the more advantageous conditions contained more achievement imagery.
What makes computer users spend their free time working with the computer? Are there different types of users and, if so, in what ways do they differ? N = 271 subjects took part in an online survey concerning the incentives for computer use in free time. Selected mailing lists were used to identify highly committed users (A4 3.9 hours of free time a day spent working with computers). The following incentive factors were found for these users: community/ affiliation; sense of competence; flexibility/utility; avoidance of boredom; rebellious tendency to illegality. Depending on their favorite use of the computer, three types of users were found: Purposeful users (58%), hackers (entering other networks without intention to cause damage) (22%), and crackers (entering other networks with intention to cause damage) (20%). There are significant differences in the incentive profiles of these types of users. Hacking and cracking, but not purposeful use, are correlated with flow experience and positive activation. These findings are not representative for all leisure time computer users. They refer to a sample of highly committed users who can be reached in special associations (e.g., relevant student networks, the Chaos Computer Club)
Anreizanalyse intensiver Freizeitnutzung von Computern : Hacker, Cracker und zweckorientierte Nutzer
(2006)
Was bringt intensive Computernutzer dazu, ihre Freizeit am Rechner zu verbringen, und gibt es hierbei Unterschiede zwischen verschiedenen Nutzertypen? N = 271 Personen nahmen an einer online Befragung zu Anreizen freizeitlicher Computernutzung teil. Durch ausgewählte Internetverteiler waren gezielt besonders engagierte Computernutzer angesprochen worden (M = 3,9 Freizeitstunden am Rechner pro Tag). Für diese Nutzer fanden sich (in der Reihenfolge ihres Gewichtes) folgende Anreizfaktoren: Zugehörigkeit/Gemeinschaft; Kompetenzerleben; Vielseitigkeit/Nutzen; Langeweilevermeidung; rebellische Illegalitätstendenz. Gruppiert nach ihren bevorzugten Nutzungsweisen fanden sich drei Nutzertypen: Zweckorientierte Nutzer (58%), Hacker (= Eindringen in fremde Systeme ohne Schädigungsabsicht, 22%) und Cracker (Eindringen mit Schädigungsabsicht, 20%). Diese Nutzertypen unterschieden sich deutlich in ihrem Anreizprofil. Hacking und Cracking, nicht aber zweckorientierte Nutzungsweisen waren korreliert mit Flow-Erleben und positiver Aktivierung am Rechner. Die Ergebnisse sind nicht repräsentativ für alle Freizeitnutzer. Sie beziehen sich auf eine gezielt rekrutierte Stichprobe besonders engagierter Computernutzer, die über spezifische Netzwerke (z. B. relevante Fachschaften, Chaos Computer Club) erreichbar sind.
What makes computer users spend their free time working with the computer? Are there different types of users and, if so, in what ways do they differ? N = 271 subjects took part in an online survey concerning the incentives for computer use in free time. Selected mailing lists were used to identify highly committed users (A4 3.9 hours of free time a day spent working with computers). The following incentive factors were found for these users: community/ affiliation; sense of competence; flexibility/utility; avoidance of boredom; rebellious tendency to illegality. Depending on their favorite use of the computer, three types of users were found: Purposeful users (58%), hackers (entering other networks without intention to cause damage) (22%), and crackers (entering other networks with intention to cause damage) (20%). There are significant differences in the incentive profiles of these types of users. Hacking and cracking, but not purposeful use, are correlated with flow experience and positive activation. These findings are not representative for all leisure time computer users. They refer to a sample of highly committed users who can be reached in special associations (e.g., relevant student networks, the Chaos Computer Club)