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Algoritma Okuryazarlığı Çalışmalarının Sistematik Literatür Taraması: Göz Ardı Edilen Boşlukların Belirlenmesi

Yıl 2025, Sayı: 48, 85 - 105, 26.12.2025
https://doi.org/10.17829/turcom.1616793

Öz

Bu sistematik literatür taraması, algoritmik sistemlerin bireylerin bilgiye erişimini, fırsatlarını ve sosyal etkileşimlerini dijital ortamlarda artan bir şekilde şekillendirdiği bağlamda, algoritma okuryazarlığı araştırmalarının mevcut durumunu incelemektedir. Çalışmanın temel amacı, kullanıcıların algoritmik sistemlere yönelik farkındalıklarına ilişkin göz ardı edilen alanları ve bilgi boşluklarını tespit etmektir. Bu amaçla, algoritma okuryazarlığının boyutlarına odaklanan toplam 47 ilgili araştırma sentezlenmiştir. Bulgular, genel algoritma farkındalık seviyelerinin toplu ortalama puanının 3.35 olduğunu, yani orta düzeyde bir bilinç seviyesi bulunduğunu göstermektedir. Bu ortalama skor, 16 kantitatif çalışmada yer alan 11.147 katılımcının verilerine dayanmaktadır. Bu ılımlı seviye, kullanıcıların algoritmaların çevrimiçi deneyimlerini etkilediğine dair temel bir farkındalığa sahip olsalar bile, algoritmaların altında yatan karmaşık mekanizmaları tam olarak anlamayabileceklerine işaret etmektedir. İncelenen araştırmalarda, sosyal medya kullanıcıları ve öğrenciler en baskın araştırma özneleri olarak öne çıkmaktadır. Platform odaklarında ise Facebook, Instagram ve TikTok, en sık çalışılan mecralardır. Analiz, araştırmaların çoğunlukla Amerika Birleşik Devletleri, Çin ve Almanya gibi gelişmiş ülkelerde yürütüldüğünü ortaya koyarak ciddi bir coğrafi dengesizliğe ve bir "algoritmik ayrıma" dikkat çekmektedir. Derleme, marjinalize edilmiş grupların dijital dahli ve etik kaygılar gibi konuları içeren, yeterince temsil edilmeyen bölgeleri ve demografik grupları kapsayan daha kapsayıcı araştırmalara olan kritik ihtiyacı vurgulamaktadır. Gelecek çalışmaların, daha adil bir dijital ortamın oluşumuna katkıda bulunmak için bu küresel eşitsizlikleri gidermeyi hedeflemesi önerilmektedir.

Kaynakça

  • Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
  • Billon, M., Marco, R., & Lera-Lopez, F. (2009). Disparities in ICT adoption: A multidimensional approach to study the cross-country digital divide. Telecommunications Policy, 33(10), 596–610. https://doi.org/10.1016/j.telpol.2009.08.006
  • 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
  • Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information Communication & Society, 20(1), 30–44. https://doi.org/10.1080/1369118X.2016.1154086
  • Bucher, T. (2018). If...Then: Algorithmic power and politics. Oxford University Press.
  • Büchi, M., Just, N., & Latzer, M. (2016). Modeling the second-level digital divide: A five-country study of social differences in Internet use. New Media & Society, 18(11), 2703–2722. https://doi.org/10.1177/1461444815604154
  • Combs, J. P., Bustamante, R. M., & Onwuegbuzie, A. J. (2010). An interactive model for facilitating development of literature reviews. International Journal of Multiple Research Approaches, 4(2), 159–182. https://doi.org/10.5172/mra.2010.4.2.159
  • Corrocher, N., & Ordanini, A. (2002). Measuring the digital divide: A framework for the analysis of cross-country differences. Journal of Information Technology, 17(1), 9–19. https://doi.org/10.1080/02683960210132061
  • de Groot, T., de Haan, M., & van Dijken, M. (2023). Learning in and about a filtered universe: Young people’s awareness and control of algorithms in social media. Learning, Media and Technology, 48(4), 701–713. https://doi.org/10.1080/17439884.2023.2253730
  • Dogruel, L. (2021). What is algorithm literacy? A conceptualization and challenges regarding its empirical measurement. In M. Taddicken & C. Schumann (Eds.), Algorithms and communication (pp. 67–93). De Gruyter. https://doi.org/10.48541/dcr.v9.3
  • Dogruel, L., Facciorusso, D., & Stark, B. (2022). “I’m still the master of the machine.” Internet users’ awareness of algorithmic decision-making and their perception of its effect on their autonomy. Information Communication & Society, 25(9), 1311–1332. https://doi.org/10.1080/1369118X.2020.1863999
  • Dogruel, L., Masur, P., & Joeckel, S. (2022). Development and validation of an algorithm literacy scale for internet users. Communication Methods and Measures, 16(2), 115–133. https://doi.org/10.1080/19312458.2021.1968361
  • Eder, M., & Sjøvaag, H. (2024). Artificial intelligence and the dawn of an algorithmic divide. Frontiers in Communication, 9(1), 1–10. https://doi.org/10.3389/fcomm.2024.1453251
  • Ernst, J. (2024). Understanding algorithmic recommendations. A qualitative study on children’s algorithm literacy in Switzerland. Information, Communication & Society, 5(3), 1–17. https://doi.org/10.1080/1369118X.2024.2382224
  • Espinoza-Rojas, J., Siles, I., & Castelain, T. (2023). How using various platforms shapes awareness of algorithms. Behaviour and Information Technology, 42(9), 1422–1433. https://doi.org/10.1080/0144929X.2022.2078224
  • Fang, W., & Jin, J. (2022). Unpacking the effects of personality traits on algorithmic awareness: The mediating role of previous knowledge and moderating role of internet use. Frontiers in Psychology, 13(1), 1–12. https://doi.org/10.3389/fpsyg.2022.953892
  • Goodrow, C. (2020, June 18). On YouTube’s recommendation system. YouTube Blog. https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/
  • Gran, A. B., Booth, P., & Bucher, T. (2021). To be or not to be algorithm aware: A question of a new digital divide? Information, Communication & Society, 24(12), 1779–1796. https://doi.org/10.1080/1369118X.2020.1736124
  • Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
  • Hu, J., & Wang, R. (2023). Familiarity breeds trust? The relationship between dating app use and trust in dating algorithms via algorithm awareness and critical algorithm perceptions. International Journal of Human-Computer Interaction. 40(17), 4596–4607. https://doi.org/10.1080/10447318.2023.2217014
  • Jain, A., Brooks, J. R., Alford, C. C., Chang, C. S., Mueller, N. M., Umscheid, C. A., & Bierman, A. S. (2023). Awareness of racial and ethnic bias and potential solutions to address bias with use of health care algorithms. JAMA Health Forum, 4(6), e231197. https://doi.org/10.1001/jamahealthforum.2023.1197
  • Kearns, M., & Roth, A. (2019). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press.
  • Livingstone, S., & Blum-Ross, A. (2020). Parenting for a digital future: How hopes and fears about technology shape children’s lives. Oxford Academic.
  • Voronina, L. V., Sergeeva, N. N., & Utyumova, E. A. (2016). Development of algorithm skills in preschool children. Procedia-Social and Behavioral Sciences, 233(1), 155–159. doi: 10.1016/j.sbspro.2016.10.176
  • Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
  • Mosseri, A. (2021, June 8). Shedding more light on how Instagram works. Instagram. https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works
  • Mubarak, F., & Suomi, R. (2022). Elderly forgotten? Digital exclusion in the information age and the rising grey digital divide. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 59(1), 1–7. https://journals.sagepub.com/doi/full/10.1177/00469580221096272
  • Nadini, M., Alessandretti, L., Di Giacinto, F., Martino, M., Aiello, L. M., & Baronchelli, A. (2021). Mapping the NFT revolution: Market trends, trade networks, and visual features. Scientific Reports, 11(1), 20902. https://doi.org/10.1038/s41598-021-00053-8
  • Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53(1), 102–104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press. Oeldorf-Hirsch, A., & Neubaum, G. (2023). What do we know about algorithmic literacy? The status quo and a research agenda for a growing field. New Media & Society, 27(2), 681–701. https://doi.org/10.1177/14614448231182662
  • Pérez-Escolar, M., Lilleker, D., & Tapia-Frade, A. (2023). A systematic literature review of the phenomenon of disinformation and misinformation. Media and Communication, 11(2), 76–87. https://doi.org/10.17645/mac.v11i2.6453
  • Petrovčič, A., Reisdorf, B. C., Vehovar, V., & Bartol, J. (2025). Disentangling the role of algorithm awareness and knowledge in digital inequalities: An empirical validation of an explanatory model. Information, Communication & Society, 28(4), 557–574. https://doi.org/10.1080/1369118X.2024.2363896
  • Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide. Blackwell Publishing.
  • Rainie, L., & Anderson, J. (2017, February 8). The need grows for algorithmic literacy, transparency and oversight. Pew Research Center. https://www.pewresearch.org/internet/2017/02/08/theme-7-the-need-grows-for-algorithmic-literacy-transparency-and-oversight/
  • Segijn, C. M., Strycharz, J., Riegelman, A., & Hennesy, C. (2021). A literature review of personalization transparency and control: Introducing the transparency–awareness–control framework. Media and Communication, 9(4), 120–133. https://doi.org/10.17645/mac.v9i4.4054
  • 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(1), 1–13 102494. https://doi.org/10.1016/j.ijinfomgt.2022.102494
  • Statista. (2024, May 17). Number of worldwide social network users 2028. Statista. https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/
  • TikTok. (2020, June 18). How TikTok recommends videos #ForYou. TikTok. https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you/
  • Voorveld, H. A. M., Meppelink, C. S., & Boerman, S. C. (2023). Consumers’ persuasion knowledge of algorithms in social media advertising: Identifying consumer groups based on awareness, appropriateness, and coping ability. International Journal of Advertising, 43(6), 960–986. https://doi.org/10.1080/02650487.2023.2264045
  • X. (2023, March 31). Twitter’s recommendation algorithm. X. https://blog.x.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm
  • Zarouali, B., Boerman, S. C., & De Vreese, C. H. (2021). Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale (AMCA-scale). Telematics and Informatics, 62(1), 1–12. https://doi.org/10.1016/j.tele.2021.101607
  • Zarouali, B., Helberger, N., & De Vreese, C. H. (2021). Investigating algorithmic misconceptions in a media context: Source of a new digital divide?. Media and Communication, 9(4), 134–144. https://doi.org/10.17645/mac.v9i4.4090
  • Zhang, X., & and Jin, H. (2023). How does smart technology, artificial intelligence, automation, robotics, and algorithms (STAARA) awareness affect hotel employees’ career perceptions? A disruptive innovation theory perspective. Journal of Hospitality Marketing & Management, 32(2), 264–283. https://doi.org/10.1080/19368623.2023.2166186

Systematic Literature Review of Algorithm Literacy Studies: Mapping The Overlooked Gaps

Yıl 2025, Sayı: 48, 85 - 105, 26.12.2025
https://doi.org/10.17829/turcom.1616793

Öz

Considering the increasing role of algorithmic systems in shaping individuals’ access to information, social interactions, and opportunities in digital environments, this systematic literature review investigates the present condition of algorithm literacy research. The primary aim of the study is to identify overlooked areas, conceptual blind spots, and persistent knowledge gaps regarding users’ awareness and understanding of algorithmic systems. To achieve this, the review synthesizes a total of 47 relevant studies, each addressing different dimensions of algorithm literacy and offering insights into how individuals perceive, interpret, and respond to algorithmic mechanisms embedded in digital platforms. The findings reveal an overall mean algorithm awareness score of 3.35, indicating a moderate level of awareness based on data from 11.147 participants across 16 quantitative studies. This moderate level suggests that users generally recognize the influence of algorithms on their online experiences, yet many still struggle to grasp how these systems operate or shape content distribution, personalization, and decision making processes. The analysis further shows that social media users and university students constitute the dominant research groups, while platforms such as Facebook, Instagram, and TikTok are most frequently examined. A notable geographical imbalance also emerges, since much of the existing research is concentrated in developed countries including the United States, China, and Germany, pointing to an expanding “algorithmic divide”.

Kaynakça

  • Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
  • Billon, M., Marco, R., & Lera-Lopez, F. (2009). Disparities in ICT adoption: A multidimensional approach to study the cross-country digital divide. Telecommunications Policy, 33(10), 596–610. https://doi.org/10.1016/j.telpol.2009.08.006
  • 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
  • Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information Communication & Society, 20(1), 30–44. https://doi.org/10.1080/1369118X.2016.1154086
  • Bucher, T. (2018). If...Then: Algorithmic power and politics. Oxford University Press.
  • Büchi, M., Just, N., & Latzer, M. (2016). Modeling the second-level digital divide: A five-country study of social differences in Internet use. New Media & Society, 18(11), 2703–2722. https://doi.org/10.1177/1461444815604154
  • Combs, J. P., Bustamante, R. M., & Onwuegbuzie, A. J. (2010). An interactive model for facilitating development of literature reviews. International Journal of Multiple Research Approaches, 4(2), 159–182. https://doi.org/10.5172/mra.2010.4.2.159
  • Corrocher, N., & Ordanini, A. (2002). Measuring the digital divide: A framework for the analysis of cross-country differences. Journal of Information Technology, 17(1), 9–19. https://doi.org/10.1080/02683960210132061
  • de Groot, T., de Haan, M., & van Dijken, M. (2023). Learning in and about a filtered universe: Young people’s awareness and control of algorithms in social media. Learning, Media and Technology, 48(4), 701–713. https://doi.org/10.1080/17439884.2023.2253730
  • Dogruel, L. (2021). What is algorithm literacy? A conceptualization and challenges regarding its empirical measurement. In M. Taddicken & C. Schumann (Eds.), Algorithms and communication (pp. 67–93). De Gruyter. https://doi.org/10.48541/dcr.v9.3
  • Dogruel, L., Facciorusso, D., & Stark, B. (2022). “I’m still the master of the machine.” Internet users’ awareness of algorithmic decision-making and their perception of its effect on their autonomy. Information Communication & Society, 25(9), 1311–1332. https://doi.org/10.1080/1369118X.2020.1863999
  • Dogruel, L., Masur, P., & Joeckel, S. (2022). Development and validation of an algorithm literacy scale for internet users. Communication Methods and Measures, 16(2), 115–133. https://doi.org/10.1080/19312458.2021.1968361
  • Eder, M., & Sjøvaag, H. (2024). Artificial intelligence and the dawn of an algorithmic divide. Frontiers in Communication, 9(1), 1–10. https://doi.org/10.3389/fcomm.2024.1453251
  • Ernst, J. (2024). Understanding algorithmic recommendations. A qualitative study on children’s algorithm literacy in Switzerland. Information, Communication & Society, 5(3), 1–17. https://doi.org/10.1080/1369118X.2024.2382224
  • Espinoza-Rojas, J., Siles, I., & Castelain, T. (2023). How using various platforms shapes awareness of algorithms. Behaviour and Information Technology, 42(9), 1422–1433. https://doi.org/10.1080/0144929X.2022.2078224
  • Fang, W., & Jin, J. (2022). Unpacking the effects of personality traits on algorithmic awareness: The mediating role of previous knowledge and moderating role of internet use. Frontiers in Psychology, 13(1), 1–12. https://doi.org/10.3389/fpsyg.2022.953892
  • Goodrow, C. (2020, June 18). On YouTube’s recommendation system. YouTube Blog. https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/
  • Gran, A. B., Booth, P., & Bucher, T. (2021). To be or not to be algorithm aware: A question of a new digital divide? Information, Communication & Society, 24(12), 1779–1796. https://doi.org/10.1080/1369118X.2020.1736124
  • Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
  • Hu, J., & Wang, R. (2023). Familiarity breeds trust? The relationship between dating app use and trust in dating algorithms via algorithm awareness and critical algorithm perceptions. International Journal of Human-Computer Interaction. 40(17), 4596–4607. https://doi.org/10.1080/10447318.2023.2217014
  • Jain, A., Brooks, J. R., Alford, C. C., Chang, C. S., Mueller, N. M., Umscheid, C. A., & Bierman, A. S. (2023). Awareness of racial and ethnic bias and potential solutions to address bias with use of health care algorithms. JAMA Health Forum, 4(6), e231197. https://doi.org/10.1001/jamahealthforum.2023.1197
  • Kearns, M., & Roth, A. (2019). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press.
  • Livingstone, S., & Blum-Ross, A. (2020). Parenting for a digital future: How hopes and fears about technology shape children’s lives. Oxford Academic.
  • Voronina, L. V., Sergeeva, N. N., & Utyumova, E. A. (2016). Development of algorithm skills in preschool children. Procedia-Social and Behavioral Sciences, 233(1), 155–159. doi: 10.1016/j.sbspro.2016.10.176
  • Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
  • Mosseri, A. (2021, June 8). Shedding more light on how Instagram works. Instagram. https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works
  • Mubarak, F., & Suomi, R. (2022). Elderly forgotten? Digital exclusion in the information age and the rising grey digital divide. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 59(1), 1–7. https://journals.sagepub.com/doi/full/10.1177/00469580221096272
  • Nadini, M., Alessandretti, L., Di Giacinto, F., Martino, M., Aiello, L. M., & Baronchelli, A. (2021). Mapping the NFT revolution: Market trends, trade networks, and visual features. Scientific Reports, 11(1), 20902. https://doi.org/10.1038/s41598-021-00053-8
  • Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53(1), 102–104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press. Oeldorf-Hirsch, A., & Neubaum, G. (2023). What do we know about algorithmic literacy? The status quo and a research agenda for a growing field. New Media & Society, 27(2), 681–701. https://doi.org/10.1177/14614448231182662
  • Pérez-Escolar, M., Lilleker, D., & Tapia-Frade, A. (2023). A systematic literature review of the phenomenon of disinformation and misinformation. Media and Communication, 11(2), 76–87. https://doi.org/10.17645/mac.v11i2.6453
  • Petrovčič, A., Reisdorf, B. C., Vehovar, V., & Bartol, J. (2025). Disentangling the role of algorithm awareness and knowledge in digital inequalities: An empirical validation of an explanatory model. Information, Communication & Society, 28(4), 557–574. https://doi.org/10.1080/1369118X.2024.2363896
  • Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide. Blackwell Publishing.
  • Rainie, L., & Anderson, J. (2017, February 8). The need grows for algorithmic literacy, transparency and oversight. Pew Research Center. https://www.pewresearch.org/internet/2017/02/08/theme-7-the-need-grows-for-algorithmic-literacy-transparency-and-oversight/
  • Segijn, C. M., Strycharz, J., Riegelman, A., & Hennesy, C. (2021). A literature review of personalization transparency and control: Introducing the transparency–awareness–control framework. Media and Communication, 9(4), 120–133. https://doi.org/10.17645/mac.v9i4.4054
  • 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(1), 1–13 102494. https://doi.org/10.1016/j.ijinfomgt.2022.102494
  • Statista. (2024, May 17). Number of worldwide social network users 2028. Statista. https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/
  • TikTok. (2020, June 18). How TikTok recommends videos #ForYou. TikTok. https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you/
  • Voorveld, H. A. M., Meppelink, C. S., & Boerman, S. C. (2023). Consumers’ persuasion knowledge of algorithms in social media advertising: Identifying consumer groups based on awareness, appropriateness, and coping ability. International Journal of Advertising, 43(6), 960–986. https://doi.org/10.1080/02650487.2023.2264045
  • X. (2023, March 31). Twitter’s recommendation algorithm. X. https://blog.x.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm
  • Zarouali, B., Boerman, S. C., & De Vreese, C. H. (2021). Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale (AMCA-scale). Telematics and Informatics, 62(1), 1–12. https://doi.org/10.1016/j.tele.2021.101607
  • Zarouali, B., Helberger, N., & De Vreese, C. H. (2021). Investigating algorithmic misconceptions in a media context: Source of a new digital divide?. Media and Communication, 9(4), 134–144. https://doi.org/10.17645/mac.v9i4.4090
  • Zhang, X., & and Jin, H. (2023). How does smart technology, artificial intelligence, automation, robotics, and algorithms (STAARA) awareness affect hotel employees’ career perceptions? A disruptive innovation theory perspective. Journal of Hospitality Marketing & Management, 32(2), 264–283. https://doi.org/10.1080/19368623.2023.2166186
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Medya Okuryazarlığı, Yeni Medya
Bölüm Araştırma Makalesi
Yazarlar

Esra Bozkanat 0000-0002-6050-2550

Gönderilme Tarihi 9 Ocak 2025
Kabul Tarihi 25 Temmuz 2025
Yayımlanma Tarihi 26 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 48

Kaynak Göster

APA Bozkanat, E. (2025). Systematic Literature Review of Algorithm Literacy Studies: Mapping The Overlooked Gaps. Türkiye İletişim Araştırmaları Dergisi(48), 85-105. https://doi.org/10.17829/turcom.1616793

Türkiye İletişim Araştırmaları Dergisi'nde yayımlanan tüm makaleler Creative Commons Atıf-Gayri Ticari 4.0 Uluslararası Lisansı ile lisanslanmıştır.