A Research On Algorıthm Lıteracy Of New Medıa Department Students

Yıl 2024, , 155 - 180, 30.01.2024

Muhammet Kemal Karaman , İlker Yiğit

https://doi.org/10.17680/erciyesiletisim.1338510

Öz

Algorithms and their advanced forms, including artificial intelligence, have an increasing scope in every field, especially internet services, and their expanding impacts are affecting users in various ways. In online environments, algorithms provide personalized content according to users' needs and desires, making life easier, but at the same time, they also pose various risks, especially for unaware users. In order to consciously benefit from the positive effects of algorithms and to protect against the negative effects of non-transparent algorithmic environments, it is necessary to determine and measure algorithm literacy competencies. Determining and measuring these competencies are necessary in terms of establishing the required academic frameworks for practical applications in this field. For this purpose, this study investigates the algorithm literacy levels of students in the New Media Department at Usak University. A three-item questionnaire scale based on the Rasch model was used in the research. The analyses were conducted using R Studio and Excel programs. As a result of the analyses, it has been observed that, in general, the algorithm awareness levels of the study group are higher than the algorithm knowledge levels. Additionally, the research results demonstrate significant differences in algorithm literacy based on participants' demographic characteristics.

Anahtar Kelimeler

Algorithm Literacy, Algorithm Awareness, Algorithm Knowledge, Rasch Analysis.

Yeni Medya Bölümü Öğrencilerinin Algoritma Okuryazarlıkları Üzerine Bir Araştırma

Öz

Algoritmaların ve gelişmiş formları olan yapay zekânın başta internet servisleri olmak üzere her alanda artan bir kapsama alanı ve genişleyen etkileri kullanıcıları çeşitli yönlerden etkilemektedir. Çevrim içi ortamlarda algoritmalar, bir taraftan kullanıcıların ihtiyaç ve isteklerine göre kişiselleştirilmiş içerikler sunarken hayatı kolaylaştırmakta, bir taraftan da özellikle bilinçsiz kullanıcılar için çeşitli riskler barındırmaktadır. Gerek algoritmaların olumlu etkilerinden bilinçli bir şekilde faydalanabilmek için, gerekse de algoritmik ortamların şeffaf olmayan yapılarının olumsuz etkilerinden korunabilmek için algoritma okuryazarlığı yeterliliklerinin belirlenmesi ve ölçümlenmesi gerekmektedir. Bu yeterliliklerin belirlenmesi ve ölçümlenmesi, bu alana yönelik pratik uygulamalar için gerekli akademik çerçevelerin oluşturulması açısından gereklidir. Bu amaçla bu çalışmada, Uşak Üniversitesi Yeni Medya Bölümü öğrencilerinin algoritma okuryazarlık düzeyleri araştırılmıştır. Araştırmada Rasch modeli tabanlı üç şıklı ölçek kullanılmıştır. Analizler R Studio ve Excel programlarında yapılmıştır. Yapılan analizler sonucunda, katılımcı grubu için genel olarak algoritma farkındalığı düzeyleri ortalamalarının, algoritma bilgisi düzeyi ortalamalarından daha yüksek olduğu görülmüştür. Ayrıca araştırma sonuçları, katılımcıların demografik özelliklerine göre algoritma okuryazarlıklarında belirgin farklılıklar olduğunu göstermektedir.

Anahtar Kelimeler

Algoritma Okuryazarlığı, Algoritma Farkındalığı, Algoritma Bilgisi, Rasch Analizi.

Kaynakça

• Bacalja, A., Beavis, C., & O’Brien, A. (2022). Shifting landscapes of digital literacy. The Australian Journal of Language and Literacy, 45(2), 253-263. https://doi.org/10.1007/s44020-022-00027-x
• Bond, T., Yan, Z., & Heene, M. (2020). Applying the Rasch model: Fundamental measurement in the human sciences (4rd ed.). Routledge.
• Brodsky, J. E., Zomberg, D., Powers, K. L., & Brooks, P. J. (2020). Assessing and fostering college students’ algorithm awareness across online contexts. Journal of Media Literacy Education, 12(3), 43-57. https://doi.org/10.23860/JMLE-2020-12-3-5
• Bruns, A. (2019). Are filter bubbles real? John Wiley & Sons.
• Burrell, J., Kahn, Z., Jonas, A., & Griffin, D. (2019). When users control the algorithms: Values expressed in practices on twitter. Proceedings of the ACM on human-computer interaction, 3(CSCW), 1-20. https://doi.org/10.1145/3359240
• Cetina Presuel, R., & Sierra, J. M. M. (2019). Algorithms and the news: Social media platforms as news publishers and distributors. Cetina Presuel, R., & Martínez Sierra, J.(2019). Algorithms and the News: Social Media Platforms as News Publishers and Distributors. Revista De Comunicación, 18(2), 261-285. https://doi.org/10.26441/RC18.2-2019-A13
• Cook, K. F., O’Malley, K. J., & Roddey, T. S. (2005). Dynamic assessment of health outcomes: Time to let the CAT out of the bag? Health services research, 40(5p2), 1694-1711. https://doi.org/10.1111/j.1475-6773.2005.00446.x
• Cotter, K. (2019). Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New media & society, 21(4), 895-913. https://doi.org/10.1177/1461444818815684
• Cotter, K. (2022). Practical knowledge of algorithms: The case of BreadTube. new media & society, 0(0). https://doi.org/10.1177/14614448221081802
• Debelak, R., Strobl, C., & Zeigenfuse, M. D. (2022). An introduction to the rasch model with examples in r. Crc Press.
• DeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022). Toward User-Driven Algorithm Auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 1-19. https://doi.org/10.1145/3491102.3517441
• Dogruel, L. (2021). What is Algorithm Literacy? A Conceptualization and Challenges Regarding its Empirical Measurement. 75898, 9, 67-93. https://doi.org/10.48541/dcr.v9.3
• Dogruel, L., Masur, P., & Joeckel, S. (2021). Development and validation of an algorithm literacy scale for internet users. Communication Methods and Measures, 115-133. https://doi.org/10.1080/19312458.2021.1968361
• Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., Hamilton, K., & Sandvig, C. (2015). “ I always assumed that I wasn’t really that close to [her]” Reasoning about Invisible Algorithms in News Feeds. Proceedings of the 33rd annual ACM conference on human factors in computing systems, 153-162. https://doi.org/10.1145/2702123.2702556
• Ferrari, A., & Punie, Y. (2013). DIGCOMP: A framework for developing and understanding digital competence in Europe. Publications Office of the European Union Luxembourg.
• Gillespie, T. (2014). The relevance of algorithms. Media technologies: Essays on communication, materiality, and society, 167(2014), 167.
• Gruber, J., & Hargittai, E. (2023). The importance of algorithm skills for informed Internet use. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231168100
• Hambleton, R. K., & Jones, R. W. (1993). Comparison of classical test theory and item response theory and their applications to test development. Educational measurement: issues and practice, 12(3), 38-47.
• Hern, A. (2020). Twitter apologises for’racist’image-cropping algorithm. The Guardian (Sept. 2020). https://www. theguardian. com/technology/2020/sep/21/twitter-apologises-for-racist-image-cropping-algorithm.
• Kaya, A., Balay, R., & Göçen, A. (2012). Öğretmenlerin alternatif ölçme ve değerlendirme tekniklerine ilişkin bilme, uygulama ve eğitim ihtiyacı düzeyleri. International Journal of Human Sciences, 9(2), 1229-1259.
• Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410. https://doi.org/10.5465/annals.2018.0174
• Kılıç, S. (2013). Örnekleme Yöntemleri. Journal of Mood Disorders, 3(1), 44-46. https://doi.org/10.5455/jmood.20130325011730
• Koenig, A. (2020). The algorithms know me and i know them: Using student journals to uncover algorithmic literacy awareness. Computers and Composition, 58, 102611. https://doi.org/10.1016/j.compcom.2020.102611
• Linacre, J. M. (2002). What do infit and outfit, mean-square and standardized mean. Rasch measurement transactions, 16(2), 878.
• Linacre, J. M. (2014). Winsteps (Version 3.81. 0). Beaverton, Oregon: Winsteps. com.
• Margetts, H., Lehdonvirta, V., González-Bailón, S., Hutchinson, J., Bright, J., Nash, V., & Sutcliffe, D. (2021). The Internet and public policy: Future directions. Policy & Internet, 13(2), 162-184. https://doi.org/10.1002/poi3.263
• Morris, P. (2022). Teaching Algorithmic Literacy within a Media Literacy Program. https://scholarworks.iupui.edu/handle/1805/34329
• Moylan, R., & Code, J. (2023). Algorithmic Futures: An analysis of teacher professional digital competence frameworks through an algorithm literacy lens. 1-32.
• Musiani, F. (2013). Governance by algorithms. Internet Policy Review, 2(3). https://doi.org/10.14763/2013.3.188
• Oeldorf-Hirsch, A., & Neubaum, G. (2021). What do we know about algorithmic literacy? The status quo and a research agenda for a growing field. 1-39. https://osf.io/2fd4j/download
• Oon, P.-T., Spencer, B., & Kam, C. C. S. (2017). Psychometric quality of a student evaluation of teaching survey in higher education. Assessment & Evaluation in Higher Education, 42(5), 788-800. https://doi.org/10.1080/02602938.2016.1193119
• Rasch, G. (1993). Probabilistic models for some intelligence and attainment tests. ERIC.
• Ridley, M., & Pawlick-Potts, D. (2021). Algorithmic literacy and the role for libraries. Information technology and libraries, 40(2). https://doi.org/10.6017/ital.v40i2.12963
• Shin, D., Kee, K. F., & Shin, E. Y. (2022). Algorithm awareness: Why user awareness is critical for personal privacy in the adoption of algorithmic platforms? International Journal of Information Management, 65, 102494. https://doi.org/10.1016/j.ijinfomgt.2022.102494
• Shin, D., Rasul, A., & Fotiadis, A. (2022). Why am I seeing this? Deconstructing algorithm literacy through the lens of users. Internet Research, 32(4), 1214-1234. https://doi.org/10.1108/INTR-02-2021-0087
• Smith, A. B., Wright, P., Selby, P., & Velikova, G. (2007). Measuring social difficulties in routine patient-centred assessment: A Rasch analysis of the social difficulties inventory. Quality of Life Research, 16, 823-831. https://doi.org/10.1007/s11136-007-9181-9
• Susser, D., Roessler, B., & Nissenbaum, H. (2019). Technology, autonomy, and manipulation. Internet Policy Review, 8(2), 1-22. https://doi.org/10.14763/2019.2.1410
• Taylor, S. H., & Choi, M. (2022). An Initial Conceptualization of Algorithm Responsiveness: Comparing Perceptions of Algorithms Across Social Media Platforms. Social Media+ Society, 8(4), 20563051221144322. https://doi.org/10.1177/20563051221144322
• Thorson, K., Cotter, K., Medeiros, M., & Pak, C. (2021). Algorithmic inference, political interest, and exposure to news and politics on Facebook. Information, Communication & Society, 24(2), 183-200. https://doi.org/10.1080/1369118X.2019.1642934
• Tornabene, R. E., Sbeglia, G. C., & Nehm, R. H. (2020). Measuring belief in genetic determinism: A psychometric evaluation of the PUGGS instrument. Science & Education, 29(6), 1621-1657. https://doi.org/10.1007/s11191-020-00146-2
• Verma, S. (2019). Weapons of math destruction: How big data increases inequality and threatens democracy. Vikalpa, 44(2), 97-98. https://doi.org/10.1177/0256090919853933
• Vigdor, N. (2019). Apple card investigated after gender discrimination complaints. The New York Times, 10.
• Wright, B. D., & Stone, M. H. (1999). Measurement Essentials. Wilmington, DE: Wide Range. Inc.[Google Scholar].