Abstract
This paper presents and delineates Promoting Equity and Achievement in Real-time Learning (PEARL), a framework developed to help strengthen academic and career-related skills of students and professionals, and most specifically those from underserved and underrepresented backgrounds. PEARL uses problem-based learning pedagogy, competency-based education and artificial intelligence to break learning and assessment activities into manageable learning chunks. This allows for the formation, creation, and validation of stackable knowledge units. The paper also highlights how artificial intelligence along with learner-centered pedagogy can be used to improve knowledge gain and skill mastery.
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References
Anderson, L., Krathwohl, D.: A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Abridged Longman, New York (2001)
Greeno, J.: The situativity of knowing, learning, and research. Am. Psychol. 53(1), 5–26 (1998)
Salomon, G.: Technology’s promises and dangers in a psychological and educational context. In: De Vaney, A. (Ed.), Theory to Practice: Technology and the Culture of Classrooms, pp. 4–10 (1998)
Guilbaud, P., Camp, J., Bruegge, A.V.: Digital badges as micro-credentials: an opportunity to improve learning or just another education technology fad?. In: Winthrop Conference on Teaching and Learning, vol. 25 (2016)
Levine, E., Patrick, S.: What is competency-based education? An Updated Definition. Vienna, VA: Aurora Institute (2019)
Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84(2), 191–215 (1977)
Ackerman, P., Heggestad, E.: Intelligence, personality, and interests: evidence for overlapping traits. Psychol. Bull. 121(2), 219–245 (1997)
Nodine, T.: How did we get here? a brief history of competency-based higher education in the United States. J. Competency-Based Educ. 1, 5–11 (2016)
Derryberry, A., Everhart, D., Knight, E.: Badges and competencies: new currency for professional credentials. In: Muilenberg, L., Berge, Z. (eds.) Digital Badges in Education: Trends, Issues, and Cases, pp. 12–20 (2016)
Pitt, C., Bell, A., Strickman, R., Davis, K.: Supporting learners’ STEM-oriented career pathways with digital badges. Inf. Learn. Sci. (2018)
Aberdour, M.: Transforming workplace learning culture with digital badges. In: Ifenthaler, D., Bellin-Mularski, N., Mah, D.-K. (eds.) Foundation of Digital Badges and Micro-Credentials, pp. 203–219. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-15425-1_11
Davis, K., Singh, S.: Digital badges in afterschool learning: documenting the perspectives and experiences of students and educators. Comput. Educ. 88, 72–83 (2015)
Abramovich, S., Wardrip, P.: Impact of badges in motivation to learn. In: Muilenberg, L., Berge, Z. (eds.) Digital Badges in Education: Trends, Issues, and Cases, pp. 53–61 (2016)
Ifenthaler, D., Bellin-Mularski, N., Mah, D.-K. (eds.): Foundation of Digital Badges and Micro-Credentials: Demonstrating and Recognizing Knowledge and Competencies. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-15425-1
West-Puckett, S.: Making classroom writing assessment more visible, equitable, and portable through digital badging. Coll. Engl. 79(2), 127–151 (2016)
Chemers, M., Zurbriggen, E., Syed, M., Goza, B., Bearman, S.: The role of efficacy and identity in science career commitment among underrepresented minority students. J. Soc. Issues 67, 469–491 (2011)
Carpi, A., Ronan, D., Falconer, H., Lents, N.: Cultivating Minority scientists: undergraduate research increases self-efficacy and career ambitions for underrepresented students in STEM. J. Res. Sci. Teach. 54, 169–194 (2017)
Guilbaud, P., Bubar, E., Langran, E.: STEM excellence and equity in K-12 settings: use of augmented reality-based educational experiences to promote academic achievement and learner success. In: Stephanidis, C., Antona, M., Ntoa, S. (eds.) HCII 2021. CCIS, vol. 1421, pp. 45–50. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78645-8_6
Beede, D., et al.: Education Supports Racial and Ethnic Equality in STEM. U.S. Department of Commerce, Economics and Statistics Administration, Washington (2011)
Chickering, A., Gamson, Z.: Seven principles for good practice in undergraduate education. In: Chickering , A., Gamson, Z. (eds.) Applying the Seven Principles for Good Practice in Undergraduate Education, pp. 63–69 (1991)
Grasha, A.: Teaching with Style: A Practical Guide to Enhancing Learning by Understanding Teaching and Learning Styles. Alliance Publishers, Pittsburgh (1996)
Aoun, J.: Robot-Proof: Higher Education in the Age of Artificial Intelligence. U.S (2017)
Wang, Y.: Education in a Changing World: Flexibility, Skills and Employability. World Bank, Washington DC (2002)
DiRenzo, M., Greenhaus, J., Weer, C.: Relationship between protean career orientation and work life balance: a resource perspective. J. Organ. Behav. 36, 538–560 (2015)
Carr, P., Walton, M.: Cues of working together fuel intrinsic motivation. J. Exp. Soc. Psychol. 53, 169–184 (2014)
Kuron, L., Schweitzer, L., Lyons, S., Ng, E.: Career profiles in the “new career”: evidence of their prevalence and correlates. Career Dev. Int. 21, 355–377 (2016)
Bialik, M., Fadel, C.: Skills for the 21st Century: What Should Students Learn? Center for Curriculum Redesign. Boston, Massachusetts (2015). http://www.curriculumredesign
Jessop, B.: The knowledge economy as a state project. In: The Nation-State in Transformation, pp. 110–129. Aarhus University Press, Århus (2010)
Strada Institute and Emsi: Robot-Ready: Human Skills for the Future of Work (2018)
Connell, G., Donovan, D., Chambers, T.: Increasing the use of student-centered pedagogies from moderate to high improves student learning and attitudes about biology. CBE Life Sci. Educ. 15(1) (2016)
Miri, B., David, B., Uri, Z.: Purposely teaching for the promotion of higher-order thinking skills: a case of critical thinking. Res. Sci. Educ. 37(4), 353–369 (2007)
Montealegre, R., Cascio, W.: Technology-driven changes in work and employment. Commun. ACM 60, 60–67 (2017)
Pusca, D., Bowers, R., Northwood, D.: Hands-on experiences in engineering classes: the need, the implementation and the results. 15, 12–18 (2017)
Nilson, L.: Creating Self-Regulated Learners: Strategies to Strengthen Students’ Self-Awareness and Learning. Sterling, VA: Stylus (2013)
Rotgans, J., Schmidt, H.: Situational interest and academic achievement in the active-learning classroom. Learn. Instr. 21(1), 58–67 (2011)
Cattel, R.: Intelligence: Its Structure, Growth, and Action. Elsevier Science Publishers, BV, Amsterdam, The Netherlands (1987)
Evans, J.: In two minds: dual-process accounts of reasoning. Trends Cogn. Sci. 7(10), 454–459 (2003)
Hmelo-Silver, C.: Problem-based learning: what and how do students learn? Educ. Psychol. Rev. 16(3), 235–266 (2004)
Lave, J., Wenger, E.: Situated Learning: Legitimate Peripheral Participation. Cambridge University Press, Cambridge (1991)
Guskey, T.: Mastery Learning in 21st Century Education: A reference Handbook, vol. 1 (2009)
Robles, M.: Executive perceptions of the top 10 soft skills needed in today’s workplace. Bus. Commun. Q. 75(4), 453–465 (2012)
Davidson, K.: Employers find ‘soft skills’ like critical thinking in short supply. Wall Street J. 3 (2016)
Strauss, V.: The Surprising Thing Google Learned About its Employees—and What it Means for Today’s Students. The Washington Post (2017)
Karimi, H., Pina, A.: Strategically addressing the soft skills gap among STEM undergraduates. J. Res. STEM Educ. 7(1), 21–46 (2021)
Fadde, P., Klein, G.: Accelerating expertise using action learning activities. Cogn. Technol. 17(1), 11–18 (2012)
Ericsson, K.: The influence of experience and deliberate practice on the development of superior expert performance. In: Ericsson, K., Charness, N., Hoffman, R., Feltovich, P. (eds.) The Cambridge Handbook of Expertise and Expert Performance, pp. 683–703 (2006)
Ericsson, K., Charness, N.: Expertise: Its structure and acquisition. Am. Psychol. 49, 725–747 (1994)
Wallace, D.: Knowledge Management: Historical and Cross-Disciplinary Themes (2007)
Hildreth, P., Kimble, C.: Knowledge networks: innovation through communities of practice, vol. 9 (2004)
Onyon, C.: Problem-based learning: a review of the educational and psychological theory. Clin. Teach. 9(1), 22–26 (2012)
Loyens, S., Kirschner, P., Paas, F.: Problem-based learning. In: Harris, K., Graham, S. Urdan, T. (eds.) APA Educational Psychology Handbook, vol. 2 (2011)
Winarno, S., Muthu, K., Ling, L.: Direct PBL (DPBL): a framework for Integrating direct Instruction and PBL approach. Int. Educ. Stud. 11(1), 119 (2017)
Walton, J., Ryerse, M.: Competency-based education: definitions and difference makers. Getting Smart (2017)
Bliven, A., Jungbauer, M.: The impact of student recognition of excellence to student outcome in a competency-based educational model. J. Competency-Based Educ. (JCBE). 6(4), 195–205 (2021)
Henri, M., Johnson, M., Nepal, B.: A review of competency-based learning: Tools, assessments, and recommendations. J. Eng. Educ. 106(4), 607–638 (2017)
Burke, J.: Competence-based education and training (1989)
Levine, E., Patrick, S.: What is competency-based education? an updated definition (2019)
U.S. Department of Education Office of Career, Technical, and Adult Education, Advancing Career and Technical Education in State and Local Career Pathways Systems (2015)
Zoogah, B.: Historicizing management and organization in Africa. Acad. Manage. Learn. Educ. 20(3), 382–406 (2021)
Kotsiantis, S.: Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artif. Intell. Rev. 37(4), 331–344 (2012). https://doi.org/10.1007/s10462-011-9234-x
Hua, W., Cuiqin, M., Lijuan, Z.: A brief review of machine learning and its application. In: IEEE International Conference on Information Engineering and Computer Science (2009)
Ferlitsch, A.: Deep Learning Patterns and Practices (2021)
Hirsch, M.J., Crowder, J.A.: Machine learning to augment the fusion process for data classification. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds.) NAFIPS 2021. LNNS, vol. 258, pp. 154–165. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-82099-2_14
Doyle, J., Francis, B., Tannenbaum, A.: Feedback Control Theory (1992)
Piaget, J.: Cognitive development in children: development and learning. J. Res. Sci. Teach. 2, 176–186 (1964)
Lewis, M., Michalson, L.: Children’s Emotions and Moods: Developmental Theory and Measurement. Plenum Press, New York (1983)
Kremenitzer, J.P., Miller, R.: Are you a highly qualified emotionally intelligent early childhood educator? Young Children 63, 106–112 (2008)
David, S.Y.: Social and emotional learning programs for adolescents. Future Child. 27(1), 73–94 (2017)
Bronfenbrenner, U.: The Ecology of Human Development Experiments by Nature and Design. Harvard University Press, Cambridge (1996)
Welsh, J.A., Nix, R.L., Blair, C., Bierman, K.L., Nelson, K.E.: The development of cognitive skills and gains in academic school readiness for children from low-income families. J. Educ. Psychol. 102(1), 43–53 (2010)
Pantsar, M. The enculturated move from proto-arithmetic to arithmetic. Front. Psychol. 10 (2019)
Colchester, K., Hagras, H., Alghazzawi, D., Aldabbagh, G.: A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. J. Artif. Intell. Soft Comput. Res. 7(1), 47–64 (2016)
Brusilovsky, P.: Adaptive and intelligent web-based educational systems. Int. J. Artif. Intell. Educ. 13(2–4), 159–172 (2003)
Diziol, D., Walker, E., Rummel, N., Koedinger, K.: Using intelligent tutor technology to implement adaptive support for student collaboration. Educ. Psychol. Rev. 22(1), 89–102 (2010). https://doi.org/10.1007/s10648-009-9116-9
Du Boulay, B.: Artificial intelligence as an effective classroom assistant. IEEE Intell. Syst. 31(6), 76–81 (2016)
Kolb, D.: Experiential Learning: Experience as the Source of Learning and Development, Second Edition (2014)
Barrows, H.: Problem-based learning in medicine and beyond: a brief overview. In: Wilkerson, L., Gijselaers, W. (eds.) New Directions for Teaching and Learning, vol. 68, pp. 3–11 (1996)
Bayat, S., Tarmizi, A.: Effects of problem-based learning approach on cognitive variables of university students. Procedia Soc. Behav. Sci. 46, 3146–3151 (2012)
Ericsson, K., Chamess, N.: Expertise: its structure and acquisition. Am. Psychol. 23 (1994)
Starr, L.: Integrating Technology in the Classroom: it Takes more than just having Computers (2011)
Park, S., Ertmer, P.: Examining barriers in technology-enhanced problem-based learning: Using a performance support systems approach. Br. J. Edu. Technol. 39, 631–643 (2008)
Rackley, C.: Mapping knowledge units using a Learning Management System (LMS) course framework. KSU Proc. Cybersecurity Educ. Res. Pract. 7 (2018)
Centers of Academic Excellence in Cyber Defense: Knowledge Units (2019)
Iwata, J., Clayton, J., Saravani, S.: Learner autonomy, microcredentials and self-reflection: a review of a Moodle-based medical English review course. Int. J. Inf. Commun. Technol. 10, 42–50 (2017)
Fink, D.: Creating significant learning experiences: an integrated approach to designing college courses (2003)
Sweet, C., Blythe, H., Phillips, B., Carpenter, R.: Transforming Your Students into Deep Learners: A Guide for Instructors (2016)
Potvin, P., Hasni, A.: Interest, motivation and attitude towards science and technology at K-12 levels: a systematic review of 12 years of educational research. Stud. Sci. Educ. 50(1), 85–129 (2014)
Kolb, D.: Learning Style Inventory (1976)
Henderson, M., Selwyn, N., Aston, R.: What works and why? student perceptions of ‘useful’ digital technology in university teaching and learning. Stud. High. Educ. 42(8), 1567–1579 (2017)
Gagne, R.: The Conditions of Learning, 4th (edn.) (1985)
Bybee, R., Powell, J., Trowbridge, L.: Teaching Secondary School Science: Strategies for Developing Scientific Literacy (2008)
Magana, A.: Learning strategies and multimedia techniques for scaffolding size and scale cognition. Comput. Educ. 72, 367–377 (2014)
Koedinger, K., Corbett, A., Perfetti, C.: The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Cogn. Sci. 36(5), 757–798 (2012)
Keengwe, J., Onchwari, G., Wachira, P.: The use of computer tools to support meaningful learning. AACE J. 16(1), 77–92 (2008)
Torp, L., Sage, S.: Problems as Possibilities: Problem-Based Learning for K-12 Education, 2nd (edn.) (2002)
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Guilbaud, P., Hirsch, M.J. (2022). Promoting Equity and Achievement in Real-Time Learning (PEARL): Towards a Framework for the Formation, Creation, and Validation of Stackable Knowledge Units. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2022. Lecture Notes in Computer Science, vol 13332. Springer, Cham. https://doi.org/10.1007/978-3-031-05887-5_12
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