Commitment is a multi-dimensional construct that has been extensively researched in the context of organizations. Organizational and professional commitment have been positively associated with technical performance, client service, attention to detail, and degree of involvement with one’s job. However, there is a relative dearth of research in terms of team commitment, especially in educational settings. Teamwork is considered a 21stcentury skill and higher education institutions are focusing on helping students to develop teamwork skills by applied projects in the coursework. But studies have demonstrated that creating a team is not enough to help students build teamwork skills. Literature supports the use of team contracts to bolster commitment, among team members. However, the relationship between team contracts and team commitment has not been formally operationalized.This research category study presents a mixed-methods approach towards characterizing and operationalizing team commitment exhibited by students enrolled in a sophomore-level systems analysis and design course by analyzing team contracts and team retrospective reflections. The course covers concepts pertaining to information systems development and includes a semester-long team project where the students work together in four or five member teams to develop the project deliverables. The students have prior software development experiences through an introductory systems development course as well as multiple programming courses. The data for this study was collected through the team contracts signed by students belonging to one of the 23 teams of this course. The study aims to answer the following research question: How can team commitment be characterized in a sophomore-level system analysis and design course among the student teams?A rubric was developed to quantify the team commitment levels of students based on their responses on the team contracts. Students were classified as high or low commitment based on the rubric scores. The emergent themes of high and low commitment teams were also presented. The results indicated that the high commitment teams were focused on setting goals, effective communication, and having mechanisms in place for timely feedback and improvement. On the other hand, low commitment teams did not articulate the goals of the project, they demonstrated a lack of dedication for attending team meetings regularly, working as a team, and had a lack of proper coordination while working together.
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Place‐based civic science—collective environmental action and solidarity for eco‐resilience
Educating children and young people (CYP) from marginalized communities about environmental crises poses a unique dilemma as educators strive to prepare them to deal with the climate crisis without compounding the stressors and fear of an unlivable future many already face. We explored how place‐based civic science (PBCS) can provide opportunities to engage youth in environmental understanding and action through teamwork in which youth feel that they belong to a group larger than themselves and gain a sense of hope from working with others toward shared goals. We argue that combining PCBS pedagogies of collective action and collaborative learning spaces can help to buffer against distress as CYP grapple with global environmental crises.
We drew from qualitative responses (student reflections and public presentations) of 486 6–12th graders (majority students of color) on what they learned from participating in PBCS projects. Projects involved egalitarian partnerships between adults from environmental organizations, teachers and student teams studying and acting together to mitigate problems and presenting their efforts in public venues.
Students’ qualitative responses revealed an identification with their team and its goal forged through the work, respect for their voice, belief in their capacity and confidence to take collective action and even enjoyment of working together to address community concerns.
PBCS through collective learning/action in student teams and nonhierarchical intergenerational partnerships, and connections that CYP forge with organizations in the broader community, can help to build CYP’s agency and efficacy while addressing “emotionally heavy” issues such as climate change.
- Award ID(s):
- 1759291
- NSF-PAR ID:
- 10448096
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Child and Adolescent Mental Health
- Volume:
- 27
- Issue:
- 1
- ISSN:
- 1475-357X
- Page Range / eLocation ID:
- p. 39-46
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.
Practitioner notes What is already known about this topic
Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.
While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.
There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.
What this paper adds
Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.
Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.
Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.
Implications for practice and/or policy
It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.
Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.
To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).
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Community and citizen science on climate change-influenced topics offers a way for participants to actively engage in understanding the changes and documenting the impacts. As in broader climate change education, a focus on the negative impacts can often leave participants feeling a sense of powerlessness. In large scale projects where participation is primarily limited to data collection, it is often difficult for volunteers to see how the data can inform decision making that can help create a positive future. In this paper, we propose and test a method of linking community and citizen science engagement to thinking about and planning for the future through scenarios story development using the data collected by the volunteers. We used a youth focused wild berry monitoring program that spanned urban and rural Alaska to test this method across diverse age levels and learning settings. Using qualitative analysis of educator interviews and youth work samples, we found that using a scenario stories development mini-workshop allowed the youth to use their own data and the data from other sites to imagine the future and possible actions to sustain berry resources for their communities. This process allowed youth to exercise key cognitive skills for sustainability, including systems thinking, futures thinking, and strategic thinking. The analysis suggested that youth would benefit from further practicing the skill of envisioning oneself as an agent of change in the environment. Educators valued working with lead scientists on the project and the experience for youth to participate in the interdisciplinary program. They also identified the combination of the berry data collection, analysis and scenarios stories activities as a teaching practice that allowed the youth to situate their citizen science participation in a personal, local and cultural context. The majority of the youth groups pursued some level of stewardship action following the activity. The most common actions included collecting additional years of berry data, communicating results to a broader community, and joining other community and citizen science projects. A few groups actually pursued solutions illustrated in the scenario stories. The pairing of community and citizen science with scenario stories development provides a promising method to connect data to action for a sustainable and resilient future.more » « less
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In this paper, we share the theories that guided the design of an interprofessional education course on Climate Change and Public Health Preparedness and how the course supported students’ professional interest and action competence as they move through their education and into their professional work in the context of our unfolding climate crisis. The course was guided by the public health emergency preparedness domains and was built to allow for students to explore applications of the content for themselves and their own profession. We designed the learning activities to support personal and professional interest development and help students move into perceived and demonstrated action competence. For the evaluation of our course, we asked the following research questions: What kinds of personal and professional commitments to action did students propose by the end of the course? Did these vary in depth and specificity and by the number of credits they enrolled in? In what ways did students develop personal and professional action competence over the course? Finally, how did they show personal, professional, and collective agency related to the course content on adaptation, preparedness, and mitigation of the health impacts from climate change? Using qualitative analysis guided by action competence and interest development theories, we coded student writing from course assignments. We also conducted comparative statistical analysis to assess differential impacts for students who enrolled for one versus three credits. The results show that this course design supported students’ progression of knowledge and perceived ability in specific individual and professional collective actions to reduce the health impacts of climate change.
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Broadband infrastructure in urban parks may serve crucial functions including an amenity to boost overall park use and a bridge to propagate WiFi access into contiguous neighborhoods. This project: SCC:PG Park WiFi as a BRIDGE to Community Resilience has developed a new model —Build Resilience through the Internet and Digital Greenspace Exposure, leveraging off-the-shelf WiFi technology, novel algorithms, community assets, and local partnerships to lower greenspace WiFi costs. This interdisciplinary work leverages: computer science, information studies, landscape architecture, and public health. Collaboration methodologies and relational definitions across disciplines are still nascent —especially when paired with civic-engaged, applied research. Student researchers (UG/Grad) are excellent partners in bridging disciplinary barriers and constraints. Their capacity to assimilate multiple frameworks has produced refinements to the project’s theoretical lenses and suggested novel socio-technical methodology improvements. Further, they are excellent ambassadors to community partners and stakeholders. In BRIDGE, we tested two mechanisms to augment student research participation. In both, we leveraged a classic, curriculum-based model named the Partnership for Action Learning in Sustainability program (PALS). This campus-wide, community-engaged initiative pairs faculty and students with community partners. PALS curates economic, environmental, and social sustainability challenges and scopes projects to customize appropriate coursework that addresses identified challenges. Outcomes include: literature searches, wireframes, and design plans that target solutions to civic problems. Constraints include the short semester timeframe and curriculum-learning-outcome constraints. (1) On BRIDGE, Dr. Kweon executed a semester-based Landscape Architecture PALS 400-level-studio. 18 undergraduates conducted in-class and in-field work to assess community needs and proposed design solutions for future park-wide WiFi. Research topics included: community-park history, neighborhood demographics, case-study analysis, and land-cover characteristics. The students conducted an in-Park, community engagement session —via interactive posterboard surveys, to gain input on what park amenities might be redesigned or added to promote WiFi use. The students then produced seven re-design plans; one included a café/garden, with an eco-corridor that integrated technology with nature. (2) From the classic, curriculum-based PALS model we created a summer-intensive for our five research assistants, to stimulate interdisciplinary collaboration in their research tasks and co-analysis of project data products: experimental technical WiFi-setup, community survey results, and stakeholder needs-assessments. Students met weekly with each other and team leadership, exchanged journal articles, and attended joint research events. This model shows promise for integrating students more formally into an interdisciplinary research project. An end-of-intensive focus group highlighted, from the students’ perspective, the pro/cons of this model. Results: In contrasting the two mechanisms, our results include: Model 1 is tried-and-trued and produces standardized, reliable products. However, as work is group based, student independence is limited —to explore topics/themes of interest. Civic groups are typically thrilled with the diversity of action plans produced. Model 2 provides greater independence in student-learning outcomes, fosters interdisciplinary, “dictionary-building” that can be used by the full team, deepens methodological approaches, and allows for student stipend payments. Lessons learned: intensive time frame needed more research team support and ideally should be extended, when possible, over the full project-span. UMD-IRB#1785365-4; NSF-award: 2125526.more » « less
