Need/Motivation (e.g., goals, gaps in knowledge)
The ESTEEM implemented a STEM building capacity project through students’ early access to a sustainable and innovative STEM Stepping Stones, called Micro-Internships (MI). The goal is to reap key benefits of a full-length internship and undergraduate research experiences in an abbreviated format, including access, success, degree completion, transfer, and recruiting and retaining more Latinx and underrepresented students into the STEM workforce. The MIs are designed with the goals to provide opportunities for students at a community college and HSI, with authentic STEM research and applied learning experiences (ALE), support for appropriate STEM pathway/career, preparation and confidence to succeed in STEM and engage in summer long REUs, and with improved outcomes. The MI projects are accessible early to more students and build momentum to better overcome critical obstacles to success. The MIs are shorter, flexibly scheduled throughout the year, easily accessible, and participation in multiple MI is encouraged. ESTEEM also establishes a sustainable and collaborative model, working with partners from BSCS Science Education, for MI’s mentor, training, compliance, and building capacity, with shared values and practices to maximize the improvement of student outcomes.
New Knowledge (e.g., hypothesis, research questions)
Research indicates that REU/internship experiences can be particularly powerful for students from Latinx and underrepresented groups in STEM. However, those experiences are difficult to access for many HSI-community college students (85% of our students hold off-campus jobs), and lack of confidence is a barrier for a majority of our students. The gap between those who can and those who cannot is the “internship access gap.” This project is at a central California Community College (CCC) and HSI, the only affordable post-secondary option in a region serving a historically underrepresented population in STEM, including 75% Hispanic, and 87% have not completed college. MI is designed to reduce inequalities inherent in the internship paradigm by providing access to professional and research skills for those underserved students. The MI has been designed to reduce barriers by offering: shorter duration (25 contact hours); flexible timing (one week to once a week over many weeks); open access/large group; and proximal location (on-campus). MI mentors participate in week-long summer workshops and ongoing monthly community of practice with the goal of co-constructing a shared vision, engaging in conversations about pedagogy and learning, and sustaining the MI program going forward.
Approach (e.g., objectives/specific aims, research methodologies, and analysis)
Research Question and Methodology: We want to know: How does participation in a micro-internship affect students’ interest and confidence to pursue STEM? We used a mixed-methods design triangulating quantitative Likert-style survey data with interpretive coding of open-responses to reveal themes in students’ motivations, attitudes toward STEM, and confidence. Participants: The study sampled students enrolled either part-time or full-time at the community college. Although each MI was classified within STEM, they were open to any interested student in any major. Demographically, participants self-identified as 70% Hispanic/Latinx, 13% Mixed-Race, and 42 female. Instrument: Student surveys were developed from two previously validated instruments that examine the impact of the MI intervention on student interest in STEM careers and pursuing internships/REUs. Also, the pre- and post (every e months to assess longitudinal outcomes) -surveys included relevant open response prompts. The surveys collected students’ demographics; interest, confidence, and motivation in pursuing a career in STEM; perceived obstacles; and past experiences with internships and MIs. 171 students responded to the pre-survey at the time of submission.
Outcomes (e.g., preliminary findings, accomplishments to date)
Because we just finished year 1, we lack at this time longitudinal data to reveal if student confidence is maintained over time and whether or not students are more likely to (i) enroll in more internships, (ii) transfer to a four-year university, or (iii) shorten the time it takes for degree attainment. For short term outcomes, students significantly Increased their confidence to continue pursuing opportunities to develop within the STEM pipeline, including full-length internships, completing STEM degrees, and applying for jobs in STEM. For example, using a 2-tailed t-test we compared means before and after the MI experience. 15 out of 16 questions that showed improvement in scores were related to student confidence to pursue STEM or perceived enjoyment of a STEM career. Finding from the free-response questions, showed that the majority of students reported enrolling in the MI to gain knowledge and experience. After the MI, 66% of students reported having gained valuable knowledge and experience, and 35% of students spoke about gaining confidence and/or momentum to pursue STEM as a career.
Broader Impacts (e.g., the participation of underrepresented minorities in STEM; development of a diverse STEM workforce, enhanced infrastructure for research and education)
The ESTEEM project has the potential for a transformational impact on STEM undergraduate education’s access and success for underrepresented and Latinx community college students, as well as for STEM capacity building at Hartnell College, a CCC and HSI, for students, faculty, professionals, and processes that foster research in STEM and education. Through sharing and transfer abilities of the ESTEEM model to similar institutions, the project has the potential to change the way students are served at an early and critical stage of their higher education experience at CCC, where one in every five community college student in the nation attends a CCC, over 67% of CCC students identify themselves with ethnic backgrounds that are not White, and 40 to 50% of University of California and California State University graduates in STEM started at a CCC, thus making it a key leverage point for recruiting and retaining a more diverse STEM workforce.
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Assessing Students’ Knowledge Co-construction Behaviors in a Collaborative Computational Modeling Environment.
Successful knowledge co-construction during collaborative learning requires students to develop a shared conceptual understanding of the domain through effective social interactions. Developing and applying shared understanding of concepts and practices is directly impacted by the prior knowledge that students bring to their interactions. We present a systematic approach to analyze students’ knowledge co-construction processes as they work through a physics curriculum that includes inquiry activities, instructional tasks, and computational model-building activities. Utilizing a combination of students’ activity logs and discourse analysis, we assess how students’ knowledge impacts their knowledge co-construction processes. We hope a better understanding of how students’ co-construction processes develop and the difficulties they face will lead to better adaptive scaffolding of students’ learning and better support for collaborative learning.
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- Award ID(s):
- 2017000
- NSF-PAR ID:
- 10348700
- Editor(s):
- Rodrigo, M.M.
- Date Published:
- Journal Name:
- AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham.
- Volume:
- 13356
- Page Range / eLocation ID:
- 515-519
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, called
Pair‐Up . We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control withPair‐Up , and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems.Practitioner notes What is already known about this topic:
Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.
Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.
What this paper adds:
Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.
Describes how we adapted six design methods to design AI features.
Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.
Implications for practice:
We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.
Our work also includes a reflection on our design process and implications for future design processes.
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Previous studies have convincingly shown that traditional, content-centered, and didactic teaching methods are not effective for developing a deep understanding and knowledge transfer. Nor does it adequately address the development of critical problem-solving skills. Active and collaborative instruction, coupled with effective means to encourage student engagement, invariably leads to better student learning outcomes irrespective of academic discipline. Despite these findings, the existing construction engineering programs, for the most part, consist of a series of fragmented courses that mainly focus on procedural skills rather than on the fundamental and conceptual knowledge that helps students become innovative problem-solvers. In addition, these courses are heavily dependent on traditional lecture-based teaching methods focused on well-structured and closed-ended problems that prepare students to plug variables into equations to get the answer. Existing programs rarely offer a systematic approach to allow students to develop a deep understanding of the engineering core concepts and discover systematic solutions for fundamental problems. Without properly understanding these core concepts, contextualized in domain-specific settings, students are not able to develop a holistic view that will help them to recognize the big picture and think outside the box to come up with creative solutions for arising problems. The long history of empirical learning in the field of construction engineering shows the significant potential of cognitive development through direct experience and reflection on what works in particular situations. Of course, the complex nature of the construction industry in the twenty-first century cannot afford an education through trial and error in the real environment. However, recent advances in computer science can help educators develop virtual environments and gamification platforms that allow students to explore various scenarios and learn from their experiences. This study aims to address this need by assessing the effectiveness of guided active exploration in a digital game environment on students’ ability to discover systematic solutions for fundamental problems in construction engineering. To address this objective, through a research project funded by the NSF Division of Engineering Education and Centers (EEC), we designed and developed a scenario-based interactive digital game, called Zebel, to guide students solve fundamental problems in construction scheduling. The proposed gamified pedagogical approach was designed based on the Constructivism learning theory and a framework that consists of six essential elements: (1) modeling; (2) reflection; (3) strategy formation; (4) scaffolded exploration; (5) debriefing; and (6) articulation. We also designed a series of pre- and post-assessment instruments for empirical data collection to assess the effectiveness of the proposed approach. The proposed gamified method was implemented in a graduate-level construction planning and scheduling course. The outcomes indicated that students with no prior knowledge of construction scheduling methods were able to discover systematic solutions for fundamental scheduling problems through their experience with the proposed gamified learning method.more » « less
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The interaction between climate and the hydrologic cycle is complex due to intricate feedback mechanisms that can have multiple impacts on key hydrologic variables. Under a changing climate, it is becoming increasingly important for undergraduate engineering students to have a better understanding of climate and the hydrologic cycle to ensure future engineering systems are more climate resilient. One way of teaching undergraduate students about these key interactions between climate and the hydrologic cycle is through numerical models that mimic these relationships. However, this is difficult to do in an undergraduate engineering course because these models are complex, and it is not feasible to devote class time and resources to teaching students the knowledge base required to run and analyze these numerical models. In addition, the recent COVID-19 pandemic required a rapid change to flexible teaching methods that can be implemented in online, hybrid, or in-person courses. To overcome these limitations, a backward design and constructive alignment approach was used to develop an active learning module in the HydroLearn framework that allows students to explore the connection between snow processes and streamflow and how this will change under different climate scenarios using numerical models and analysis. This learning module provides learning activities and tools that help the student develop a basic knowledge of snow formation and terminology, snow measurements, numerical models of snow processes, and changes in snow and streamflow under future climate. This module is particularly innovative in that it uses Google Colabs and an interactive user interface to facilitate the students' active learning in an environment that is accessible for all students and is sustainable for continued use and adaptation. This paper describes the approach, best practices and lessons learned in developing and implementing this active learning module in a remote and in-person course. In addition, it presents the results from motivation and student self-assessment surveys and discusses opportunities for improvement and further implementation that have implications for the future of hydrologic education.more » « less
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Beyond Making: Application of Constructionist Learning Principles in Engineering Prototyping CentersThe creation of student-centered spaces for making and prototyping continues to be a growing trend in higher education. These spaces are especially relevant in engineering education as they provide opportunities for engineering students to engage in authentic and collaborative problemsolving activities that can develop students’ 21st-century skills [1–3]. Principles of constructionist learning theory, which promote knowledge creation through development of a physical product [4,5], may be applied to support learning within these spaces. Beyond the construction of objects, this learning theory emphasizes a learning culture where teachers serve as guides to collaborative and student-driven learning [6]. This research seeks to understand how constructionism's learning principles are integrated into an engineering prototyping center (EPC) at a large western university. Further, we explore how these principles may support engineering student development within these spaces and identify a qualitative coding scheme for future research. Thematic analysis of semi-structured interviews with faculty, staff, and students involved with the EPC suggests that the construction of physical prototypes within this space allows for the translation of abstract concepts to concrete experiences and the development of iterative design skills. Further, the data suggests that staff play an essential role in creating a learning culture aligned with constructionist learning principles. This culture supports staff in guiding student learning, fostering a collaborative environment, and promoting students’ lifelong learning skills. Data collected within this exploratory study suggest that constructionism's learning principles can play a central role in supporting the development of engineering students in an EPC.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-11647-6_105
