An official website of the United States government
This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposed learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks.
more »
« less
When Wrong is Right: The Instructional Power of Multiple Conceptions
For many decades, educational communities, including computing education, have debated the value of telling students what they need to know (i.e., direct instruction) compared to guiding them to construct knowledge themselves (i.e., constructivism). Comparisons of these two instructional approaches have inconsistent results. Direct instruction can be more efficient for short-term performance but worse for retention and transfer. Constructivism can produce better retention and transfer, but this outcome is unreliable. To contribute to this debate, we propose a new theory to better explain these research results. Our theory, multiple conceptions theory, states that learners develop better conceptual knowledge when they are guided to compare multiple conceptions of a concept during instruction. To examine the validity of this theory, we used this lens to evaluate the literature for eight instructional techniques that guide learners to compare multiple conceptions, four from direct instruction (i.e., test-enhanced learning, erroneous examples, analogical reasoning, and refutation texts) and four from constructivism (i.e., productive failure, ambitious pedagogy, problem-based learning, and inquiry learning). We specifically searched for variations in the techniques that made them more or less successful, the mechanisms responsible, and how those mechanisms promote conceptual knowledge, which is critical for retention and transfer. To make the paper directly applicable to education, we propose instructional design principles based on the mechanisms that we identified. Moreover, we illustrate the theory by examining instructional techniques commonly used in computing education that compare multiple conceptions. Finally, we propose ways in which this theory can advance our instruction in computing and how computing education researchers can advance this general education theory.
more »
« less
- Award ID(s):
- 1941642
- PAR ID:
- 10325353
- Date Published:
- Journal Name:
- Proceedings of the Sixteenth Annual Conference on International Computing Education Research
- Page Range / eLocation ID:
- 184 to 197
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Contribution: A novel proactive and collaborative learning paradigm was proposed to engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary artificial intelligence (AI)-cybersecurity knowledge. Specifically, the proposed learning paradigm contains: 1) an immersive learning environment to motivate the students for exploring AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects and 2) a proactive education paradigm designed with the use of collaborative learning activities based on game-based learning and social constructivism. Background: Increasing evidence shows that AI techniques can be manipulated, evaded, and misled, which can result in new and profound security implications. There is an education and training gap to foster a qualified cyber-workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. Efforts have been made to incorporate a comprehensive curriculum to meet the demand. There still remain essential challenges for effectively educating students on the interaction of AI and cybersecurity. Intended Outcomes: A novel proactive and collaborative learning paradigm is proposed to educate and train a qualified cyber-workforce in this new era where security breaches, privacy violations, and AI have become commonplace. Application Design: The development of this learning paradigm is grounded in the pedagogical approaches of technology-mediated learning and social constructivism. Findings: Although the research work is still ongoing, the prototype learning paradigm has shown encouraging results in promoting the learners’ engagement in applied AI learning.more » « less
-
null (Ed.)Instruction and coursework that link engineering and psychology may enable future engineers to better understand the people they are engineering for (e.g., users and clients) and themselves as engineers (e.g., teammates). In addition, human-centered engineering education may empower engineering students to better solve problems at the intersection of technology and people. In this study, we surveyed students’ conceptions and attitudes toward human systems engineering. We aggregate responses across three survey iterations to discuss students’ knowledge and beliefs, and to consider instructional opportunities for introductory courses.more » « less
-
Research in science education with multilingual learners (MLs) has expanded rapidly. This rapid expansion can be situated within a larger dialogue about what it means to provide minoritized students with an equitable education. Whereas some conceptions of equity focus on ensuring all students have access to the knowledge, practices, and language normatively valued in K‐12 schools (equity as access), increasingly prominent conceptions focus on transforming those knowledge, practices, and language in ways that center minoritized students and their communities (equity as transformation). In this article, we argue that conceptions of equity provide a useful lens for understanding emerging research in science education with MLs and for charting a research agenda. We begin by tracing how conceptions of equity have evolved in parallel across STEM and multilingual education. Then, we provide an overview of recent developments from demographic, theoretical, and policy perspectives. In the context of these developments, we provide a conceptual synthesis of emerging research by our team of early‐career scholars in three areas: (a) learning, (b) assessment, and (c) teacher education. Within each area, we unpack the research efforts in terms of how they attend to equity as access while pushing toward equity as transformation. Finally, we propose a research agenda for science education with MLs that builds on and extends these efforts. We close by offering recommendations for making this research agenda coherent and impactful: (a) being explicit about our conceptions of equity, (b) paying attention to the interplay of structure and agency, and (c) promoting interdisciplinary collaboration.more » « less
-
null (Ed.)The construct of active learning permeates undergraduate education in science, technology, engineering, and mathematics (STEM), but despite its prevalence, the construct means different things to different people, groups, and STEM domains. To better understand active learning, we constructed this review through an innovative interdisciplinary collaboration involving research teams from psychology and discipline-based education research (DBER). Our collaboration examined active learning from two different perspectives (i.e., psychology and DBER) and surveyed the current landscape of undergraduate STEM instructional practices related to the modes of active learning and traditional lecture. On that basis, we concluded that active learning—which is commonly used to communicate an alternative to lecture and does serve a purpose in higher education classroom practice—is an umbrella term that is not particularly useful in advancing research on learning. To clarify, we synthesized a working definition of active learning that operates within an elaborative framework, which we call the construction-of-understanding ecosystem. A cornerstone of this framework is that undergraduate learners should be active agents during instruction and that the social construction of meaning plays an important role for many learners, above and beyond their individual cognitive construction of knowledge. Our proposed framework offers a coherent and actionable concept of active learning with the aim of advancing future research and practice in undergraduate STEM education.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3446871.3469750
