Student’s Rating of Contexts for Teaching Data Literacy at School regarding the Context Characteristics relation to everyday life and uniqueness
Article No.: 8, Pages 1 - 5
Abstract
Preparing young people for their future with digital competencies is an important goal for all educational systems. Competency frameworks already exist in many countries and function as guidelines for teaching at school. A central area is data and information literacy, which also plays a key role in computing (education). As a school relevant topic it should therefore be examined more closely. For any learning activity, the interest in what to learn has a major impact on learning motivation and success. But the development of interests in school subject contents is also affected by its teaching contexts. In this respect, it is an open question whether these contexts should be related close to the everyday life of the students or whether they should be unique to have a positive effect on the situational interest of the students. Results can help selecting motivational teaching contexts and to facilitate the learning process, making it easier for students to acquire the required competencies. This paper presents results of a pilot study (N = 28), which is part of a bigger research project consisting of two main studies. This pilot study belongs to the first main study. We present results on how 7th and 8th graders rated 12 contexts for teaching data literacy regarding the relation to their everyday life or uniqueness. This pilot study primarily serves to test a self developed questionnaire and its comprehensibility to use it in the first main study. It also gives first insights into expected results of the upcoming main study.
References
[1]
G. Aikenhead. 1994/00/00. What Is STS Science Teaching?In STS Education: International Perspectives on Reform. Ways of Knowing Science Series, J. Solomon and G. Aikenhead (Eds.). Teachers College Press, New York.
[2]
T. Amstad. 1978. Wie Verstaendlich Sind Unsere Zeitungen?Ph.D. Dissertation. Universität Zürich, Zürich.
[3]
R. Bamberger and A. T. Rabin. 1984. New Approaches to Readability: Austrian Research. The Reading Teacher 37, 6 (1984), 512–519.
[4]
J. Bennett. 2016. Bringing Science to Life. In Teachers Creating Context-Based Learning Environments in Science, R. Taconis, P. den Brok, and A. Pilot (Eds.). SensePublishers, Rotterdam, 21–39.
[5]
C.-H. Björnsson. 1968. Läsbarhet. Liber, Stockholm, Sweden.
[6]
A. Bollin, H. Demarle-Meusel, M. Kesselbacher, C. Mößlacher, M. Rohrer, and J. Sylle. 2018. The Bebras Contest in Austria – Do Personality, Self-Concept and General Interests Play an Influential Role?. In Informatics in Schools. Fundamentals of Computer Science and Software Engineering(ISSEP ’18), S. N. Pozdniakov and V. Dagienė (Eds.). Springer, St. Petersburg, Russia, 283–294.
[7]
C. Borowski, I. Diethelm, and H. Wilken. 2016. What Children Ask About Computers, the Internet, Robots, Mobiles, Games Etc. In Proceedings of the 11th Workshop in Primary and Secondary Computing Education(WiPSCE ’16). ACM, New York, NY, USA, 72–75.
[8]
T. Brinda, D. Tobinski, and S. Schwinem. 2017. Measuring Learners’ Interest in Computing (Education): Development of an Instrument and First Results. In Tomorrow’s Learning: Involving Everyone. Learning with and about Technologies and Computing. Springer, Dublin, Ireland, 484–493.
[9]
C. Brühwiler and P. Blatchford. 2011. Effects of Class Size and Adaptive Teaching Competency on Classroom Processes and Academic Outcome. Learning and Instruction 21, 1 (2011), 95–108.
[10]
S. Carretero, R. Vuorikari, and Y. Punie. 2017. DigComp 2.1: The Digital Competence Framework for Citizens with Eight Proficiency Levels and Examples of Use. Technical Report JRC106281. Publication Office of the European Union, Luxembourg.
[11]
L. Carter. 2006. Why Students with an Apparent Aptitude for Computer Science Don’t Choose to Major in Computer Science. ACM SIGCSE Bulletin 38, 1 (2006), 27–31.
[12]
I. Diethelm, C. Borowski, and T. Weber. 2010. Identifying Relevant CS Contexts Using the Miracle Question. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research(Koli Calling ’10). ACM, New York, NY, USA, 74–75.
[13]
K. E. Graves and L. A. DeLyser. 2017. Interested In Class, But Not In The Hallway: A Latent Class Analysis (LCA) of CS4All Student Surveys. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education(SIGCSE ’17). ACM, New York, NY, USA, 243–248.
[14]
W. Grießhaber. 2006. Supporting Learners: Profile Analysis as a Didactic Tool for Learner Language Analysis. In Investigating and Facilitating Language Learning: Papers in Honour of Lienhard Legenhausen, M. Kötter, O. Traxel, and S. Gabel (Eds.). WVT, Trier, 103–116.
[15]
W. Grießhaber. 2013. Die Profilanalyse für Deutsch als Diagnoseinstrument zur Sprachförderung. Technical Report. Kompetenzzentrum ProDaZ, Essen, Germany.
[16]
A. Grillenberger and R. Romeike. 2018. Developing a Theoretically Founded Data Literacy Competency Model. In Proceedings of the 13th Workshop in Primary and Secondary Computing Education(WiPSCE ’18). ACM, New York, NY, USA, 9:1–9:10.
[17]
S. Habig, J. Blankenburg, H. van Vorst, S. Fechner, I. Parchmann, and E. Sumfleth. 2018. Context Characteristics and Their Effects on Students’ Situational Interest in Chemistry. International Journal of Science Education 40, 10 (2018), 1154–1175.
[18]
S. Hidi and J. M. Harackiewicz. 2000. Motivating the Academically Unmotivated: A Critical Issue for the 21st Century. Review of Educational Research 70, 2 (2000), 151–179.
[19]
C. Hildebrandt and I. Diethelm. 2012. The School Experiment InTech: How to Influence Interest, Self-Concept of Ability in Informatics and Vocational Orientation. In Proceedings of the 7th Workshop in Primary and Secondary Computing Education(WiPSCE ’12). Association for Computing Machinery, Hamburg, Germany, 30–39.
[20]
K-12 Computer Science Framework. 2016. K-12 Computer Science Framework. Technical Report. ACM, New York, NY, USA.
[21]
M. Knobelsdorf and J. Tenenberg. 2013. The Context-Based Approach Inik in Light of Situated and Constructive Learning Theories. In Informatics in Schools. Sustainable Informatics Education for Pupils of All Ages(ISSEP’13). Springer, Oldenburg, Germany, 103–114.
[22]
P. J. Kpolovie, A. I. Joe, and T. Okoto. 2014. Academic Achievement Prediction: Role of Interest in Learning and Attitude towards School. International Journal of Humanities Social Sciences and Education (IJHSSE) 1, 11(2014), 73–100.
[23]
A. Krapp. 1999. Interest, Motivation and Learning: An Educational-Psychological Perspective. European Journal of Psychology of Education 14, 1 (1999), 23–40.
[24]
A. Krapp. 2002. An Educational-Psychological Theory of Interest and Its Relation to SDT. In The Handbook of Self-Determination Research, E. Deci and R. M. Ryan (Eds.). University of Rochester Press, Rochester, NY, US, 405–427.
[25]
A. Lishinski and A. Yadav. 2019. Motivation, Attitudes, and Dispositions. In The Cambridge Handbook of Computing Education Research, S. Fincher and A. Robins (Eds.). University Press, Cambridge, 801–826.
[26]
OECD. 2007. PISA 2006: Science Competencies for Tomorrow’s World: Volume 1: Analysis. OECD Publishing, Paris, France.
[27]
S. Sjøberg and C. Schreiner. 2010. The ROSE Project An Overview and Key Findings. Technical Report. University of Oslo, Oslo, Norway.
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Published: 22 November 2020
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Koli Calling '20
Koli Calling '20: 20th Koli Calling International Conference on Computing Education Research
November 19 - 22, 2020
Koli, Finland
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