22 SES 12 C, How to Teach….in HE?
According to Ruggeri/Dempster/Hanna (2011) statistics “is one of the most common topics across disciplines and levels of study” (p. 35). This is why the discipline has to be easily adaptable to a wide range of ﬁelds (Navarrete-Alvarez/Rosales-Moreno/Huete-Morales 2010). Nonetheless from a student perspective statistics is confronted with both fear and aversion against its topics, and wrong expectations (Ruggeri/Dempster/Hanna 2011). As Gruber & Renkl (1996) have clarified, especially students of social sciences often have aversions against learning statistics and they often lack of a solid background in basic mathematical procedures which complicates the didactical task. To master those challenges, both researchers and practitioners deal with the issue of teaching and learning statistics for several decades (Claine et al. 1978; Gelman & Nolan 2002; Navarrete-Alvarez/Rosales-Moreno/Huete-Morales 2010; Batanero/Burrill/Reading 2011). Since statistics is seen as an “unique subject in any curriculum and requires a distinct way of thinking” (Ruggeri/Dempster/Hanna 2011, p. 35), statistical literacy is not only restricted to school education (Watson & Callingham 2003; Wild & Pfannkuch 1999), but also addresses an essential competency in higher education (Kirsch 1997). How to teach statistics with what effect is a question to be addressed when focusing statistic lectures on university level. Research indicates that videos and video-podcasts are a valuable instrument for self-directed learning (e.g. Berk 2009; Bassill 2008; Francom/Ryan/Kariuki 2011). As a consequence of the rapid technical development and new ﬁndings in media psychology and pedagogy (Halverson & Smith 2009), videos are used and discussed in teaching context more frequently (Brophy 2004; Chenail 2011; O’Donoghue 2014). According to Vural (2013) the “...combination of images and sound creates a powerful medium for explanation of concepts while instructing learners with content that provides multiple senses“ (p. 1315). Videos and video-podcasts enable learning anytime and anywhere and allow individual learning speeds (Kay & Edwards 2012).
Since video-based learning already plays an important role in mathematics (Fößl 2014; Kay & Edwards 2012) the question arises how videos can be used in order to foster learning and learning outcome in university lectures on statistics? Research proves that learning can happen without a face-to-face instruction (Chang 2004), but it remains unclear what role the visibility of a lecturer plays in a video. Is it important that the lecturer will be seen or just be heard in the video? And for either case, how does that affect the evaluation process as well as the learning outcome? There is also a lack of research on the connection between student’s perceptions of the lecture (evaluation) and learning achievement in regards to statistics. This leads to the question how important the evaluation factors rapport (Benson et al. 2005; Marsh 1982; Murphy & Rodriguez-Manzanares 2012), teachers’ clarity (Chesebro 2003; Hines/Cruickshank/Kennedy 1985; Rodger/Murray/Cummings 2007; Hattie 2013) or students’ interest (Krapp 1999; Müller 2006; Schiefele/Krapp/Winteler 1992) might affect the learning outcome. Major goal of the study is it to examine the role and usage of learning videos in the context of teaching statistics. Research Question (1) examines to what extent the visibility of the lecturer affects the learning outcome. Research Question (2) examines to what extent the rating of clarity, structure, enthusiasm and rapport affects the learning outcome. Research Question (3) has the goal to detect those factors which predict a high overall rating.
Bassill, John N. (2008): Motivation and Cognitive Strategies in the Choice to Attend Lectures or Watch them Online. Journal of Distance Education, 22 Nr. 3, S. 129-148. Batanero, Carmen; Burrill, Gail; Reading, Chris (2011): Teaching Statistics in School Mathematics-Challenges for Teaching and Teacher Education: A Joint IC-MI/IASE Study: The 18th ICMI Study. Dordrecht: Springer. Berk, Ronald A. (2009): Multimedia Teaching with Video Clips: TV, Movies, You-Tube and mtvU in the College Classroom. International Journal of Technology in Teaching and Learning, 5 Nr. 1, S. 1-21. Brophy, J. (Hrsg.) (2004): Using Video in Teacher Education. Oxford: Elsevier. Chenail, Ronald J. (2011): YouTube as a Qualitative Research Asset: Reviewing User Generated Videos as Learning Resources. The Qualitative Report, 16 Nr. 1, S. 299. Claine, Robert et al. (1978): Statistics from Whom? Teaching Sociology, 6 Nr. 1, S. 37-46. Francom, Jeff; Ryan, Thomas G.; Kariuki, Mumbi (2011): The Effects of Podcasting on College Student Achievement and Attitude. Journal of the Research Center for Educational Technology, 7 Nr. 1, S. 39-53. Gelman, Andrew; Nolan, Deborah Ann (2002): Teaching Statistics: A Bag of Tricks. Oxford and New York: Oxford University Press. Gruber, Hans; Renkl, Alexander (1996): Alpträume sozialwissenschaftlicher Studierender: Empirische Methoden und Statistik. In: Lompscher, Joachim; Mandl, Heinz (Hrsg.): Lehr- und Lernprobleme im Studium. Bern: Huber, S. 118-130. Halverson, Richard; Smith, Anette (2009): How new Technologies have (and have not) Changed Teaching and Learning in Schools. Journal of Computing in Teacher Education, 26 Nr. 2, S. 49-54. Kirsch, Irwin S. (1997): Literacy Performance on Three Scales: Definitions and Results. In: McLennan, W. (Hrsg.): Aspects of literacy. Canberra: Australian Bureau of Statistics, S. 98-124. Navarrete-Alvarez, Esteban; Rosales-Moreno, Maria J.; Huete-Morales, Maria D. (2010): Teaching Statistics in Labor, Social, Juridical or Economic Studies. US-China Education Review, 7 Nr. 10, S. 36-41. O'Donoghue, Michael (2014): Producing Video For Teaching and Learning: Planning and Collaboration. New York: Taylor & Francis. Ruggeri, Kai; Dempster; Hanna, Donncha (2011): The Impact of Misunderstanding the Nature of Statistics. Psychology Teaching Review, 17 Nr. 1, S. 35-40. Watson, Jane; Callingham, Rosemary (2003): Statistical Literacy: A Complex Hierarchical Construct. Statistics Education Research Journal, 2 Nr. 2, S. 3-46. Wild, Chris J.; Pfannkuch, Maxine (1999): Statistical Thinking in Empirical Enquiry. International Statistical Review, 67 Nr. 3, S. 223-265. Due to 400 words, not all references can be listed.
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