Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience

Published: 01 November 2021 Publication History

Abstract

Artificial Intelligence and its sub-disciplines are becoming increasingly relevant in numerous areas of academia as well as industry and can now be considered a core area of Computer Science [84]. The Higher Education sector are offering more courses in Machine Learning and Artificial Intelligence than ever before. However, there is a lack of research pertaining to best practices for teaching in this complex domain that heavily relies on both computing and mathematical knowledge. We conducted a literature review and qualitative study with students and Higher Education lecturers from a range of educational institutions, with an aim to determine what might constitute best practices in this area in Higher Education. We hypothesised that confidence, mathematics anxiety, and differences in student educational background were key factors here. We then investigated the issues surrounding these and whether they inhibit the acquisition of knowledge and skills pertaining to the theoretical basis of artificial intelligence and machine learning. This article shares the insights from both students and lecturers with experience in the field of AI and machine learning education, with the aim to inform prospective pedagogies and studies within this domain and move toward a framework for best practice in teaching and learning of these topics.

References

[1]
Daniel Asamoah, Derek Doran, and Shu Schiller. 2015. Teaching the foundations of data science: An interdisciplinary approach. In Proceedings of the Pre-ICIS SIGDSA Workshop. 1–9. Retrieved from http://arxiv.org/abs/1512.04456.
[2]
Mark H. Ashcraft, Elizabeth P. Kirk, and Derek Hopko. 1998. On the cognitive consequences of mathematics anxiety. Dev. Math. Skills: Studies Dev. Psychol.(Nov.1998), 175–196.
[3]
Mark H. Ashcraft. 2002. Math anxiety: Personal, educational, and cognitive consequences. Curr. Direct. Psychol. Sci. 11, 5 (2002), 181–185. https://doi.org/10.1111/1467-8721.00196
[4]
Caroline Baillie, John A. Bowden, and Jan H. F. Meyer. 2013. Threshold capabilities: Threshold concepts and knowledge capability linked through variation theory. Higher Edu. 65, 2 (2013), 227–246. https://doi.org/10.1007/s10734-012-9540-5
[5]
Yehuda Baruch. 1999. Response rate in academic studies. Hum. Relat. 52, 4 (1999), 421–438.
[6]
Derek Bell. 2018. In Proceedings of the Maths Anxiety Summit. 1–21.
[7]
Elizabeth Boese. 2016. Just-in-time learning for the just google it era. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE’16). 341–345. https://doi.org/10.1145/2839509.2844583
[8]
James Bourne. 2019. 93% of firms committed to AI—but skills shortage posing problems. Retrieved from https://artificialintelligence-news.com/2019/05/21/93-of-firms-committed-to-ai-but-skills-shortage-posing-problems/.
[9]
Jonas Boustedt, S. Gävle, Anna Eckerdal, Robert Mccartney, Mark Ratcliffe, Jan Erik Moström, Kate Sanders, and Carol Zander. 2007. Threshold concepts in computer science: Do they exist and are they useful? In Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education. 504–508.
[10]
Mary Brenner. 2006. Interviewing in educational research. In Handbook of Complementary Methods in Education Research, Gregory Camilli, Judith Green, and Patricia Elmore (Eds.). Routledge, Abingdon.
[11]
Robert J. Brunner and Edward J. Kim. 2016. Teaching data science. Procedia Comput. Sci. 80 (2016), 1947–1956. https://doi.org/10.1016/j.procs.2016.05.513
[12]
Daniel Cameron and Kelly Maguire. 2017. Public views of machine learning: Digtial natives. Retrieved from https://royalsociety.org/topics-policy/projects/machine-learning/.
[13]
Jose Canas, Maria Bajo, and Pilar Gonzalvo. 1994. Mental models and computer programming. Hum. Comput. Studies 40 (1994), 795–811. https://doi.org/10.1063/1.3033202arxiv:cond-mat/0512156v1
[14]
Rosa Cera, Michela Mancini, and Alessandro Antonietti. 2014. Relationships between metacognition, self-efficacy and self-regulation in learning. Edu. Cultur. Psychol. Studies7 (2014), 115–141. https://doi.org/10.7358/ecps-2013-007-cera
[15]
Rodrigo Ceron. 2019. AI, machine learning and deep learning: What’s the difference? Retrieved from https://www.ibm.com/blogs/systems/ai-machine-learning-and-deep-learning-whats-the-difference/.
[16]
Christine Chin and David E. Brown. 2000. Learning in science: A comparison of deep and surface approaches. J. Res. Sci. Teach. 37, 2 (2000), 109–138. https://doi.org/10.1002/(SICI)1098-2736(200002)37:2%3C109::AID-TEA3%3E3.0.CO;2-7
[17]
John F. Chizmar and Anthony L. Ostrosky. 1998. The one-minute paper: Some empirical findings. J. Econ. Edu. 29, 1 (1998), 3–10. https://doi.org/10.1080/00220489809596436
[18]
Martin Coffey and Graham Gibbs. 2001. The evaluation of the student evaluation of educational quality questionnaire (SEEQ) in UK higher education. Assess. Eval. Higher Edu. 26, 1 (2001), 89–93. https://doi.org/10.1080/02602930020022318
[19]
Louis Cohen, Lawrence Manion, and Keith Morrison. 2018. Research Methods in Education (8th ed.). Routledge, NY.
[20]
Complete University Guide. [n.d.]. Top UK University League Tables and Rankings 2022 Complete University Guide. Retrieved from htts://www.thecompleteuniversityguide.co.uk/league-tables/rankings.
[21]
Glynis Cousin. 2006. An introduction to threshold concepts. Planet 17, 1 (2006), 4–5. https://doi.org/10.11120/plan.2006.00170004
[22]
Tom Crick. 2017. Computing education: An overview of research in the field. Cronfa—Swansea University Open Access Repository(Apr.2017), 1–38. Retrieved from http://cronfa.swan.ac.uk/Record/cronfa43589%0Ahttps://royalsociety.org/-/media/policy/projects/computing-education/literature-review-overview-research-field.pdf.
[23]
Paul Curzon, Peter McOwan, James Donohue, Seymour Wright, and William Marsh. 2018. Teaching of concepts. In Computer Science Education—Perspectives on Teaching and Learning in School, Sue Sentence, Erik Barendsen, and Carsten Schulte (Eds.). Bloomsbury Academic, London.
[24]
Nell B. Dale. 2006. Most difficult topics in CS1. ACM SIGCSE Bull. 38, 2 (2006), 49. https://doi.org/10.1145/1138403.1138432
[25]
Peter Davies. 2006. Threshold concepts: How can we recognise them? In Overcoming Barriers to Student Understanding, Ray Land and Jan H. F. Meyer (Eds.). Routledge, Abingdon.
[26]
Media Department for Digital and Sport. 2017. UK Digital Strategy. Technical Report. Government of the United Kingdom. Retrieved from https://www.gov.uk/government/publications/uk-digital-strategy/uk-digital-strategy.
[27]
Laura Dixon and Valerie O’Gorman. 2020. “Block teaching”—exploring lecturers’ perceptions of intensive modes of delivery in the context of undergraduate education. J. Further Higher Edu. 44, 5 (2020), 583–595. https://doi.org/10.1080/0309877X.2018.1564024
[28]
Usama Fayyad and Hamit Hamutcu. 2020. Toward foundations for data science and analytics: A knowledge framework for professional standards. Harvard Data Sci. Rev. 2, 2 (2020). https://doi.org/10.1162/99608f92.1a99e67a
[29]
Rebecca Fiebrink. 2019. Machine learning education for artists, musicians, and other creative practitioners. ACM Trans. Comput. Edu. 19, 4 (2019). https://doi.org/10.1145/3294008
[30]
Paulo Freire. 1970. Pedagogy of the Oppressed. Continuum Publishing Company, New York.
[31]
Paulo Freire. 2004. The “banking” concept of education. In Educational Foundations: An Anthology of Critical Readings, Alan S. Canestrari and Bruce A. Marlowe (Eds.). Sage, London, 99–111.
[32]
Sheila Furey, Paul Springer, and Christine Parsons. 2014. Positioning university as a brand: Distinctions between the brand promise of Russell group, 1994 group, university alliance, and million+ universities. J. Market. Higher Edu. 24, 1 (2014), 99–121. https://doi.org/10.1080/08841241.2014.919980
[33]
GOV.UK. 2020. What qualification levels mean: England, Wales and Northern Ireland. Retrieved from https://www.gov.uk/what-different-qualification-levels-mean/list-of-qualification-levels.
[34]
Gerald O. Grow. 1991. Teaching learners to be self-directed. Adult Edu. Quart. 41, 3 (1991), 125–149. https://doi.org/10.1177/0001848191041003001
[36]
J. Hardin, R. Hoerl, Nicholas J. Horton, D. Nolan, B. Baumer, O. Hall-Holt, P. Murrell, R. Peng, P. Roback, D. Temple Lang, and M. D. Ward. 2015. Data science in statistics curricula: Preparing students to “think with data.” Amer. Stat. 69, 4 (2015), 343–353. https://doi.org/10.1080/00031305.2015.1077729
[37]
John Hattie. 2012. Visible Learning For Teachers. Routledge, Abingdon.
[38]
J. Hattie, J. Biggs, and N. Purdie. 1996. Effects of learning skills interventions on student learning: A meta-analysis. Rev. Edu. Res. 66, 2 (1996), 99–136. https://doi.org/10.3102/00346543066002099
[39]
Orit Hazzan, Tami Lapidot, and Noa Ragonis. 2011. Guide to Teaching Computer Science: An Activity-based Approach. Springer, New York. 1–285. https://doi.org/10.1007/978-1-4471-6630-6
[40]
Ray Hembree. 1990. The nature, effects, and relief of mathematics anxiety. J. Res. Math. Edu. 21, 1 (1990), 33–46.
[41]
Stephanie C. Hicks and Rafael A. Irizarry. 2017. A guide to teaching data science. Amer. Stat. 1305 (2017), 1–34. https://doi.org/10.1080/00031305.2017.1356747
[43]
Thomas E. Hunt, David Clark-Carter, and David Sheffield. 2011. The development and part validation of a U.K. scale for mathematics anxiety. J. Psychoedu. Assess. 29, 5 (2011), 455–466. https://doi.org/10.1177/0734282910392892
[44]
Laura Iossi. 2007. Strategies for reducing math anxiety in post-secondary students. In Proceedings of the 6th Annual College of Education Research Conference: Urban and International Education Section. 30–35. Retrieved from http://coeweb.fiu.edu/research_conference/%5Cnhttp://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=1257&context=sferc.
[45]
Ipsos MORI. 2017. Public views of machine learning. Findings from public research and engagement conducted on behalf of the royal society. Retrieved from http://www.ipsos-mori.com/terms.
[46]
Marsha Ironsmith, Jennifer Marva, Beverly Harju, and Marion Eppler. 2003. Motivation and performance in college students enrolled in self-paced versus lecture-format remedial mathematics courses. J. Instruct. Psychol. 30, 4 (2003), 276–284. https://doi.org/10.1016/j.jaci.2012.05.050
[47]
Sachin Jain and Martin Dowson. 2009. Mathematics anxiety as a function of multidimensional self-regulation and self-efficacy. Contemp. Edu. Psychol. 34, 3 (2009), 240–249. https://doi.org/10.1016/j.cedpsych.2009.05.004
[48]
Peter Jeffcock. 2018. What’s the Difference Between AI, Machine Learning, and Deep Learning? Retrieved from https://blogs.oracle.com/bigdata/difference-ai-machine-learning-deep-learning.
[49]
Paul Jones. 2010. Introducing Neuroeducational Research. Routledge, Abingdon.
[50]
Sandra M. Juhler, Janice F. Rech, Steven G. From, and Monica M. Brogan. 1998. The effect of optional retesting on college students’ achievement in an individualized algebra course. J. Exper. Edu. 66, 2 (1998), 125–137. https://doi.org/10.1080/00220979809601399
[51]
Ray Land, Glynis Cousin, Jan H. F. Meyer, and Peter Davies. 2005. Threshold concepts and troublesome knowledge(3): Implications for course design and evaluation. Improving Student Learning and Inclusivity. Oxford University Press. https://doi.org/10.4324/9780203966273
[52]
Shiri Lavy. 2017. Who benefits from group work in higher education? An attachment theory perspective. Higher Edu. 73, 2 (2017), 175–187. https://doi.org/10.1007/s10734-016-0006-z
[53]
Yann LeCun and Martin Ford. 2018. Yann LeCun. In Architects of Intelligence. Packt Publishing, Birmingham.
[54]
Jihyun Lee. 2009. Universals and specifics of math self-concept, math self-efficacy, and math anxiety across 41 PISA 2003 participating countries. Learn. Individ. Diff. 19, 3 (2009), 355–365. https://doi.org/10.1016/j.lindif.2008.10.009
[55]
Priscilla Lee and Soohyun Nam Liao. 2021. Targeting metacognition by incorporating student-reported confidence estimates on self-assessment quizzes. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE’21). 431–437. https://doi.org/10.1145/3408877.3432377
[56]
C. W. Loo and J. L. F. Choy. 2013. Sources of self-efficacy influencing academic performance of engineering students. Amer. J. Edu. Res. 1, 3 (2013), 86–92. https://doi.org/10.12691/education-1-3-4
[57]
Ursula Lucas and Rosina Mladenovic. 2007. The potential of threshold concepts: An emerging framework for educational research and practice. London Rev. Edu. 5, 3 (2007), 237–248. https://doi.org/10.1080/14748460701661294
[58]
Rosemary Luckin. 2018. Machine Learning and Human Intelligence. UCL Institute of Education Press, London.
[59]
Andrew Mcafee and Erik Brynjolfsson. 2012. Big data: The management revolution. Harvard Business Review(Oct.2012), 1–9. Retrieved from http://tarjomefa.com/wp-content/uploads/2017/04/6539-English-TarjomeFa-1.pdf.
[60]
Alan McLean. 2009. Motivating Every Learner. SAGE Publications, London.
[61]
J. H. F. Meyer and R. Land. 2003. Threshold concepts and troublesome knowledge: Linkages to ways of thinking and practising within the disciplines. In Proceedings of the Conference on Improving Student Learning: Improving Student Learning Theory and Practice—Ten Years On.412–424. https://doi.org/10.1007/978-3-8348-9837-1
[62]
Joi L. Moore, Camille Dickson-Deane, and Krista Galyen. 2010. E-learning, online learning, and distance learning environments: Are they the same? Internet Higher Edu. 14, 2 (2010), 129–135. https://doi.org/10.1016/j.iheduc.2010.10.001
[63]
Linda Nilson. 2013. Creating Self-Regulated Learners: Strategies To Strengthen Students’ Self-Awareness and Learning Skills. Stylus Publishing, Virginia.
[64]
Lorelli S. Nowell, Jill M. Norris, Deborah E. White, and Nancy J. Moules. 2017. Thematic analysis: Striving to meet the trustworthiness criteria. Int. J. Qual. Methods 16, 1 (2017), 1–13. https://doi.org/10.1177/1609406917733847
[65]
Harold F. O’Neil and Jamal Abedi. 1996. Reliability and validity of a state metacognitive inventory: Potential for alternative assessment. J. Edu. Res. 89, 4 (1996), 234–245. https://doi.org/10.1080/00220671.1996.9941208
[66]
Julie Pallant. 2016. SPSS Survival Manual (6th ed.). Open University Press, Berkshire.
[67]
Philip David Parker, Herbert W. Marsh, Joseph Ciarrochi, Sarah Marshall, and Adel Salah Abduljabbar. 2014. Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Edu. Psychol. 34, 1 (2014), 29–48. https://doi.org/10.1080/01443410.2013.797339
[68]
Marc Prensky. 2001. Digital natives, digital immigrants. On Horizon 9, 5 (2001), 1–6.
[69]
QAA. 2019. Subject Benchmark Statement: Computing (Master’s). Technical Report October. Retrieved from https://www.qaa.ac.uk/docs/qaa/subject-benchmark-statements/subject-benchmark-statement-computing-(masters).pdf?sfvrsn=15f2c881_10.
[70]
R. Quinnell, R. Thompson, and R. J. LeBard. 2013. It’s not maths, it’s science: Exploring thinking dispositions, learning thresholds and mindfulness in science learning. Int. J. Math. Edu. Sci. Technol. 44, 6 (2013), 808–816. https://doi.org/10.1080/0020739X.2013.800598
[71]
Vennila Ramalingam, Deborah LaBelle, and Susan Wiedenbeck. 2004. Self-efficacy and mental models in learning to program. In Proceedings of the 9th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (2004), 171–175. https://doi.org/10.1145/1007996.1008042
[72]
Gerardo Ramirez, Stacy T. Shaw, and Erin A. Maloney. 2018. Math anxiety: Past research, promising interventions, and a new interpretation framework. Edu. Psychol. 53, 3 (2018), 145–164. https://doi.org/10.1080/00461520.2018.1447384
[73]
Frank C. Richardson and Richard M. Suinn. 1972. The mathematics anxiety rating scale: Psychometric data. J. Counsel. Psychol. 19, 6 (1972), 551–554. https://doi.org/10.1037/h0033456
[74]
Jane Ritchie, Jane Lewis, Carol McNaughton Nicholls, and Rachel Ormston. 2014. Qualitative Research Methods in Education (2nd ed.). SAGE Publications, London.
[75]
Anthony Robins, Janet Rountree, and Nathan Rountree. 2003. Learning and teaching programming: A review and discussion. Comput. Sci. Edu. 13, 2 (2003), 137–172. https://doi.org/10.1076/csed.13.2.137.14200
[76]
Janet Rountree and Nathan Rountree. 2009. Issues regarding threshold concepts in computer science. In Proceedings of the 11th Australasian Computing Education Conference (ACE’09).
[77]
Stuart Russell and Peter Norvig. 2013. Artificial Intelligence: A Modern Approach (3rd ed.). Pearson, England.
[78]
Jeffrey Saltz, Michael Skirpan, Casey Fiesler, Micha Gorelick, Tom Yeh, Robert Heckman, Neil Dewar, and Nathan Beard. 2019. Integrating ethics within machine learning courses. ACM Trans. Comput. Edu. 19, 4 (2019). https://doi.org/10.1145/3341164
[79]
Amar Sarkar, Ann Dowker, and Roi Cohen Kadosh. 2014. Cognitive enhancement or cognitive cost: Trait-specific outcomes of brain stimulation in the case of mathematics anxiety. J. Neurosci. 34, 50 (2014), 16605–16610. https://doi.org/10.1523/JNEUROSCI.3129-14.2014
[80]
Lauren Scharff, John Draeger, Dominique Verpoorten, Marie Devlin, S. Dvorakova, Jason Lodge, and Susan Smith. 2017. Exploring metacogntition as support for learning transfer. Teach. Learn. Enquiry 5, 1 (2017), 1–14.
[81]
Gregory Schraw and Rayne Dennison. 1994. Assessing metacognitive awareness. Contemp. Edu. Psychol. 19 (1994), 460–475.
[82]
Peter Scott. 2012. It’s 20 years since polytechnics became universities—and there’s no going back. Retrieved from https://www.theguardian.com/education/2012/sep/03/polytechnics-became-universities-1992-differentiation.
[83]
Ruxandra Serbanescu. 2017. Identifying threshold concepts in physics: Too many to count! Practice and Evidence of the Scholarship of Teaching and Learning in Higher Education 12, 2 (2017), 378–396. Retrieved from http://community.dur.ac.uk/pestlhe.learning/index.php/pestlhe/article/view/178/207.
[84]
R. Benjamin Shapiro, Rebecca Fiebrink, and Peter Norvig. 2018. Education: How machine learning impacts the undergraduate computing curriculum. Commun. ACM 61, 11 (2018), 27–29. https://doi.org/10.1145/3277567
[85]
Stanford University. 2021. Artificial Intelligence Index Report. Technical Report. https://doi.org/10.1145/3399971.3399984
[86]
Lazar Stankov, Suzanne Morony, and Yim Ping Lee. 2014. Confidence: The best non-cognitive predictor of academic achievement? Edu. Psychol. 34, 1 (2014), 9–28. https://doi.org/10.1080/01443410.2013.814194
[87]
David R. Stead. 2005. A review of the one-minute paper. Active Learn. Higher Edu. 6, 2 (2005), 118–131. https://doi.org/10.1177/1469787405054237
[88]
Kay Steven and Liz Thomas. 2019. Attracting Diversity: End of Project Report. Technical Report. Retrieved from https://www.advance-he.ac.uk/knowledge-hub/attracting-diversity-end-project-report.
[89]
Janet A. Taylor and Joan Mohr. 2001. Mathematics for math anxious students studying at a distance. J. Develop. Edu. 25, 1 (2001), 30–37. Retrieved from http://web.a.ebscohost.com/sas/detail?sid=c1259a70-60b0-4cf7-a17e-b9cf790fc1cf%40sessionmgr4005&vid=0&hid=4214&bdata=JkF1dGhUeXBlPQ%3D%3D#AN=5223062&db=afh.
[90]
Matt Tendre and Mikko Apiola. 2013. Three computing traditions in school computing education. In Improving Computer Science Education, Djordje Kadijevich, Charoula Angeli, and Carsten Schulte (Eds.). Routledge, New York.
[91]
[92]
The Observer. 2003. Ivy League for the UK. Retrieved from https://www.theguardian.com/education/2003/sep/21/highereducation.uk1.
[93]
The Royal Society. 2017. Machine Learning: The Power and Promise of Computers That Learn by Example. Vol. 66. 125 pages. https://doi.org/10.1126/scitranslmed.3002564
[94]
UK AI Council. 2021. AI Roadmap. Technical Report. Retrieved from https://www.gov.uk/government/publications/ai-roadmap.
[95]
Simone Volet. 1991. Modelling and coaching of relevant metacognitive strategies for enhancing university students’ learning. Learn. Instruct. 1, 4 (1991), 319–336.
[96]
Guy Walker. 2013. A cognitive approach to threshold concepts. Higher Edu. 65, 2 (2013), 247–263. https://doi.org/10.1007/s10734-012-9541-4
[97]
Alice Yeo, Robin Legard, Jill Keegan, Kit Ward, Carol McNaughton Nicholls, and Jane Lewis. 2014. In-depth interviews. In Qualitative Research Practice, Jane Ritchie, Jane Lewis, McNaughton Nicholls, and Rachel Ormston (Eds.). SAGE Publications, London.
[98]
Erin Young, Judy Wajcman, and Laila Sprejer. 2021. Where are the Women Mapping the Gender Job Gap in AI. Policy Briefing: Full Report. Technical Report. The Alan Turing Institute. Retrieved from https://www.turing.ac.uk/research/publications/report-where-are-women-mapping-gender-job-gap-ai?_cldee=Yi5hbGxlbjJAbmV3Y2FzdGxlLmFjLnVr&recipientid=contact-37781e7a802be811811a70106faad2f1-b6a1772674d34b249cfb106c2a7b42d5&esid=e6bc640a-8090-eb11-b1ac-000d.

Cited By

View all
  • (2024)Examining the Professional Knowledge on Ethical Integration of Artificial Intelligence-Based Tools on Higher Education Instructors' Attitudes Toward Artificial Intelligence in the Context of the Fourth Industrial RevolutionEuropean Journal of Arts, Humanities and Social Sciences10.59324/ejahss.2024.1(5).081:5(121-132)Online publication date: 1-Sep-2024
  • (2024)A Holistic Perspective on the AI-Education Nexus: A Science Mapping StudyUluslararası Türk Eğitim Bilimleri Dergisi10.46778/goputeb.152227712:3(1080-1114)Online publication date: 30-Nov-2024
  • (2024)Budget Transparency and Accountability PlatformInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24APR977(3288-3306)Online publication date: 20-Jun-2024
  • Show More Cited By

Index Terms

  1. Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Transactions on Computing Education
      ACM Transactions on Computing Education  Volume 22, Issue 2
      June 2022
      312 pages
      EISSN:1946-6226
      DOI:10.1145/3494072
      • Editor:
      • Amy J. Ko
      Issue’s Table of Contents

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 01 November 2021
      Accepted: 01 August 2021
      Revised: 01 July 2021
      Received: 01 July 2020
      Published in TOCE Volume 22, Issue 2

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Artificial intelligence
      2. pedagogy
      3. self-efficacy

      Qualifiers

      • Research-article
      • Refereed

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)1,018
      • Downloads (Last 6 weeks)70
      Reflects downloads up to 13 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Examining the Professional Knowledge on Ethical Integration of Artificial Intelligence-Based Tools on Higher Education Instructors' Attitudes Toward Artificial Intelligence in the Context of the Fourth Industrial RevolutionEuropean Journal of Arts, Humanities and Social Sciences10.59324/ejahss.2024.1(5).081:5(121-132)Online publication date: 1-Sep-2024
      • (2024)A Holistic Perspective on the AI-Education Nexus: A Science Mapping StudyUluslararası Türk Eğitim Bilimleri Dergisi10.46778/goputeb.152227712:3(1080-1114)Online publication date: 30-Nov-2024
      • (2024)Budget Transparency and Accountability PlatformInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24APR977(3288-3306)Online publication date: 20-Jun-2024
      • (2024)Designing a Competency-Focused Course on Applied AI Based on Advanced System Research on Business RequirementsApplied Sciences10.3390/app1410410714:10(4107)Online publication date: 12-May-2024
      • (2024)A Study of Teaching Strategies Optimized with the Integration of Artificial Intelligence TechnologiesApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-11959:1Online publication date: 22-May-2024
      • (2024)Research on the Integration Path and Practice of AI Intelligent Technology and English Teaching Reform in Higher Vocational Colleges and UniversitiesApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-04429:1Online publication date: 26-Feb-2024
      • (2024)Unveiling the Potential: Experts' Perspectives on Artificial Intelligence Integration in Higher EducationEuropean Journal of Educational Research10.12973/eu-jer.13.4.1477volume-13-2024:volume-13-issue-4-october-2024(1477-1492)Online publication date: 15-Oct-2024
      • (2024)Enhancing AI Education for Business Students through Extended Reality: An Exploratory StudyProceedings of Mensch und Computer 202410.1145/3670653.3677506(599-604)Online publication date: 1-Sep-2024
      • (2024)Redefining the Concept of Literacy: a DigCompEdu extension for Critical Engagement with AI tools2024 9th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)10.1109/SEEDA-CECNSM63478.2024.00026(98-102)Online publication date: 20-Sep-2024
      • (2024)Advancements, Challenges, and Emerging Trends in Computer Science Education: A Systematic Literature Review of Academic and Professional Learning: Editorial2024 IEEE International Conference on Software Analysis, Evolution and Reengineering - Companion (SANER-C)10.1109/SANER-C62648.2024.00006(1-12)Online publication date: 12-Mar-2024
      • Show More Cited By

      View Options

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Full Text

      View this article in Full Text.

      Full Text

      HTML Format

      View this article in HTML Format.

      HTML Format

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media