Several consensus reports cite a critical need to dramatically increase the number and diversity of STEM graduates over the next decade. They conclude that a change to evidence-based instructional practices, such as concept-based active learning, is needed. Concept-based active learning involves the use of activity-based pedagogies whose primary objectives are to make students value deep conceptual understanding (instead of only factual knowledge) and then to facilitate their development of that understanding. Concept-based active learning has been shown to increase academic engagement and student achievement, to significantly improve student retention in academic programs, and to reduce the performance gap of underrepresented students. Fostering students' mastery of fundamental concepts is central to real world problem solving, including several elements of engineering practice. Unfortunately, simply proving that these instructional practices are more effective than traditional methods for promoting student learning, for increasing retention in academic programs, and for improving ability in professional practice is not enough to ensure widespread pedagogical change. In fact, the biggest challenge to improving STEM education is not the need to develop more effective instructional practices, but to find ways to get faculty to adopt the evidence-based pedagogies that already exist. In this project we seek to propagate the Concept Warehouse, a technological innovation designed to foster concept-based active learning, into Mechanical Engineering (ME) and to study student learning with this tool in five diverse institutional settings. The Concept Warehouse (CW) is a web-based instructional tool that we developed for Chemical Engineering (ChE) faculty. It houses over 3,500 ConcepTests, which are short questions that can rapidly be deployed to engage students in concept-oriented thinking and/or to assess students’ conceptual knowledge, along with more extensive concept-based active learning tools. The CW has grown rapidly during this project and now has over 1,600 faculty accounts and over 37,000 student users. New ConcepTests were created during the current reporting period; the current numbers of questions for Statics, Dynamics, and Mechanics of Materials are 342, 410, and 41, respectively. A detailed review process is in progress, and will continue through the no-cost extension year, to refine question clarity and to identify types of new questions to fill gaps in content coverage. There have been 497 new faculty accounts created after June 30, 2018, and 3,035 unique students have answered these mechanics questions in the CW. We continue to analyze instructor interviews, focusing on 11 cases, all of whom participated in the CW Community of Practice (CoP). For six participants, we were able to compare use of the CW both before and after participating in professional development activities (workshops and/or a community or practice). Interview results have been coded and are currently being analyzed. To examine student learning, we recruited faculty to participate in deploying four common questions in both statics and dynamics. In statics, each instructor agreed to deploy the same four questions (one each for Rigid Body Equilibrium, Trusses, Frames, and Friction) among their overall deployments of the CW. In addition to answering the question, students were also asked to provide a written explanation to explain their reasoning, to rate the confidence of their answers, and to rate the degree to which the questions were clear and promoted deep thinking. The analysis to date has resulted in a Work-In-Progress paper presented at ASEE 2022, reporting a cross-case comparison of two instructors and a Work-In-Progress paper to be presented at ASEE 2023 analyzing students’ metacognitive reflections of concept questions.
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Towards Designing Shared Digital Forensics Instructional Materials
This paper presents a systematic approach to designing
digital forensics instructional materials to address
the severe shortage of active learning materials in the digital
forensics community. The materials include real-world scenario-based
case studies, hands-on problem-driven labs for each
case study, and an integrated forensic investigation environment.
In this paper, we first clarify some fundamental concepts related
to digital forensics, such as digital forensic artifacts, artifact
generators, and evidence. We then re-categorize knowledge units
of digital forensics based on the artifact generators for measuring
the coverage of learning outcomes and topics. Finally, we utilize
a real-world cybercrime scenario to demonstrate how knowledge
units, digital forensics topics, concepts, artifacts, and investigation
tools can be infused into each lab through active learning. The
repository of the instructional materials is publicly available on
GitHub. It has gained nearly 600 stars and 22k views within
several months.
Index Terms
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- Award ID(s):
- 2039288
- NSF-PAR ID:
- 10404816
- Date Published:
- Journal Name:
- the IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC'22)
- Page Range / eLocation ID:
- 117-122
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract This paper describes the design, implementation and research of the Cyber Sleuth Science Lab (CSSL), an innovative educational program and supporting virtual learning environment, that combines pedagogical theory, gender inclusive instruction strategies, scientific principles/practices, gamification methods, computational thinking, and real-world problem solving. This program provides underrepresented youth, especially girls, with digital forensic knowledge, skills and career pathways, challenging them to explore complex social issues related to technology and to become cyber sleuths using real-world digital forensic methods and tools to solve investigative scenarios. Students also learn about related careers while improving their cyber street smarts. The CSSL incorporates additional “outside of the computer” activities to strengthen students’ engagement such as structured in-classroom discussions, mock trials, and in-person interactions with practitioner role models. The CSSL was piloted in various forms to assess the suitability for in-school and out-of-school settings, and the students predominantly represented racial minorities. Research in this project relied on a mixed methods approach for data collection and analysis, including qualitative and quantitative methods, reinforced using learning analytics generated from the students clicking through the interface and interacting with the system. Analysis of gathered data indicate that the virtual learning environment developed in this project is highly effective for teaching digital forensic knowledge, skills, and abilities that are directly applicable in the workplace. Furthermore, the strategies for gender inclusive STEM instruction implemented in CSSL are effective for engaging girls without being harmful to boys’ engagement. Learning STEM through digital forensic science taps into girls’ motivations to address real-world problems that have direct relevance to their lives, and to protect and serve their community. After participating in the educational program, girls expressed a significantly greater increase in interest, relative to boys, in learning more about careers related to digital forensics and cybersecurity.
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One of the key knowledge areas in Computer Science (CS) is Digital Logic and Computer Architecture where the learning outcome is an understanding of Boolean algebra, logic gates, registers, or arithmetic logic units, etc. and explaining how software and hardware are related to a computing system. Experimental Centric based Instructional Pedagogy (ECP) with portable laboratory instrumentation might provide real hands-on experience to obtain a practical understanding of those concepts at a lower cost compared with virtual hands-on laboratories that lack direct interaction with real apparatus or no integration of labs in the course. This work presents the initial adaptation of ECP to introduce the fundamentals of digital logic concepts in a Computer Architecture course in Spring 2022 for the first time in a CS department at a university teaching such courses without a lab and serving predominantly minority students. To establish a conducive and dynamic classroom environment by discovering course content through exploration, students majoring in CS were introduced to several logic gate types, worked with breadboards to connect circuits, and carried out operations to produce the necessary output using the commercial ADALM 1K Active Learning Module. To evaluate the impact of the ECP on students; performance in the class, three different evaluation methods were used, such as classroom observation, a signature assignment, and a Motivated Strategies for Learning Questionnaire (MSLQ) survey. The Classroom Observation Protocol for Undergraduate STEM (COPUS) findings indicated greater student engagement when ECP is used; the Signature assignment results indicated improved learning outcomes for students; and the MLSQ survey, which measures students; motivation, critical thinking, curiosity, collaboration, and metacognition, determined a positive impact of the ECP on the CS participants.more » « less
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Abstract Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education.
Context and implications Rationale for this study In 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials.
Why the new findings matter The process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application.
Implications for educational researchers and policy makers The progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences.
Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.
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