Title: The Learner Data Institute—Conceptualization: A Progress Report
This paper provides a progress report on the first 18 months of Phase 1, the conceptualization phase, of the Learner Data Institute (LDI; www.learnerdatainstitute.org). LDI is currently in Phase 1, the conceptualization phase, to be followed by Phase 2, the institute or convergence phase. The current 2-year conceptualization phase has two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering for the future institute, and (2) use the framework to provide prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. By targeting a critical mass of key challenges that are at a tipping point, LDI aims to start a chain reaction that will transform the whole learning ecosystem. We will emphasize here the key elements of the LDI science convergence framework that our team developed, implemented, and now is in the process of evaluating and refining. We highlight important outcomes of the convergence framework and related processes, including a 5-year plan for the institute phase and data-intensive prototype solutions to transform the learning ecosystem. more »« less
Rus, V.(
, Proceedings of The Second Workshop of the Learner Data Institute , The 14th International Conference on Educational Data Mining (EDM 2021))
null
(Ed.)
This paper provides a progress report on the first 18 months of Phase 1, the conceptualization phase, of the Learner Data Institute (LDI; www.learnerdatainstitute.org). LDI is currently in Phase 1, the conceptualization phase, to be followed by Phase 2, the institute or convergence phase. The current 2-year conceptualization phase has two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering for the future institute, and (2) use the framework to provide prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. By targeting a critical mass of key challenges that are at a tipping point, LDI aims to start a chain reaction that will transform the whole learning ecosystem. We will emphasize here the key elements of the LDI science convergence framework that our team developed, implemented, and now is in the process of evaluating and refining. We highlight important outcomes of the convergence framework and related processes, including a 5-year plan for the institute phase and data-intensive prototype solutions to transform the learning ecosystem.
Rus, V.; Fancsali, S.E.; Pavlik, Jr.; Venugopal, D.; Ritter, S.; Bowman, D.; The LDI Team(
, The Learner Data Institute: Emerging Science Convergence and Research Opportunities, Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022))
This paper provides an update of the Learner Data Institute (LDI; www.learnerdatainstitute.org) which is now in its third year since conceptualization. Funded as a conceptualization project, the LDI’s first two years had two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering and (2) use the framework to start developing prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. One major focus in the third, current year is synthesizing efforts from the first two years to identify new opportunities for future research by various mutual interest groups within LDI, which have focused on developing a particular prototype solution to one or more related core challenges in learning science and engineering. In addition to highlighting emerging data-intensive solutions and innovations from the LDI’s first two years, including places where LDI researchers have received additional funding for future research, we highlight here various core challenges our team has identified as being at a “tipping point.” Tipping point challenges are those for which timely investment in data-intensive approaches has the maximum potential for a transformative effect.
Strimel, G.; Pruim, D.; Briller, S.; Martinez, R.; Lucas, D.; Kelley, T.; Sohn, J.(
, Review directory American Society for Engineering Education)
There have been numerous demands for enhancements in the way undergraduate learning occurs today,
especially at a time when the value of higher education continues to be called into question (The Boyer
2030 Commission, 2022). One type of demand has been for the increased integration of
subjects/disciplines around relevant issues/topics—with a more recent trend of seeking transdisciplinary
learning experiences for students (Sheets, 2016; American Association for the Advancement of Science,
2019). Transdisciplinary learning can be viewed as the holistic way of working equally across disciplines
to transcend their own disciplinary boundaries to form new conceptual understandings as well as develop
new ways in which to address complex topics or challenges (Ertas, Maxwell, Rainey, & Tanik, 2003;
Park & Son, 2010). This transdisciplinary approach can be important as humanity’s problems are not
typically discipline specific and require the convergence of competencies to lead to innovative thinking
across fields of study. However, higher education continues to be siloed which makes the authentic
teaching of converging topics, such as innovation, human-technology interactions, climate concerns, or
harnessing the data revolution, organizationally difficult (Birx, 2019; Serdyukov, 2017). For example,
working across a university’s academic units to collaboratively teach, or co-teach, around topics of
convergence are likely to be rejected by the university systems that have been built upon longstanding
traditions. While disciplinary expertise is necessary and one of higher education’s strengths, the structures
and academic rigidity that come along with the disciplinary silos can prevent modifications/improvements
to the roles of academic units/disciplines that could better prepare students for the future of both work and
learning. The balancing of disciplinary structure with transdisciplinary approaches to solving problems
and learning is a challenge that must be persistently addressed. These institutional challenges will only
continue to limit universities seeking toward scaling transdisciplinary programs and experimenting with
novel ways to enhance the value of higher education for students and society. This then restricts
innovations to teaching and also hinders the sharing of important practices across disciplines.
To address these concerns, a National Science Foundation Improving Undergraduate STEM Education
project team, which is the topic of this paper, has set the goal of developing/implementing/testing an
authentically transdisciplinary, and scalable educational model in an effort to help guide the
transformation of traditional undergraduate learning to span academics silos. This educational model,
referred to as the Mission, Meaning, Making (M3) program, is specifically focused on teaching the crosscutting
practices of innovation by a) implementing co-teaching and co-learning from faculty and students
across different academic units/colleges as well as b) offering learning experiences spanning multiple
semesters that immerse students in a community that can nourish both their learning and innovative ideas.
As a collaborative initiative, the M3 program is designed to synergize key strengths of an institution’s
engineering/technology, liberal arts, and business colleges/units to create a transformative undergraduate
experience focused on the pursuit of innovation—one that reaches the broader campus community,
regardless of students’ backgrounds or majors. Throughout the development of this model, research was
conducted to help identify institutional barriers toward creating such a cross-college program at a
research-intensive public university along with uncovering ways in which to address these barriers. While
data can show how students value and enjoy transdisciplinary experiences, universities are not likely to be
structured in a way to support these educational initiatives and they will face challenges throughout their
lifespan. These challenges can result from administration turnover whereas mutual agreements across
colleges may then vanish, continued disputes over academic territory, and challenges over resource
allotments. Essentially, there may be little to no incentives for academic departments to engage in
transdisciplinary programming within the existing structures of higher education. However, some insights
and practices have emerged from this research project that can be useful in moving toward
transdisciplinary learning around topics of convergence. Accordingly, the paper will highlight features of
an educational model that spans disciplines along with the workarounds to current institutional barriers.
This paper will also provide lessons learned related to 1) the potential pitfalls with educational
programming becoming “un-disciplinary” rather than transdisciplinary, 2) ways in which to incentivize
departments/faculty to engage in transdisciplinary efforts, and 3) new structures within higher education
that can be used to help faculty/students/staff to more easily converge to increase access to learning across
academic boundaries.
Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020s this will only become increasingly relevant throughout this decade. Meeting this sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g. Unix, version control,C++, continuous integration). The second is knowledge of domain specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving more specialized techniques. These include parallel programming, machine learning and data science tools, and techniques to preserve software projects at all scales. This paper dis-cusses the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients from which solutions to the computing challenges of HEP can be formed. Beyond serving the community by ensuring that members are able to pursue research goals, this program serves individuals by providing intellectual capital and transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP
Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020s this will only become increasingly relevant throughout this decade. Meeting this sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g. Unix, version control,C++, continuous integration). The second is knowledge of domain specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving more specialized techniques. These include parallel programming, machine learning and data science tools, and techniques to preserve software projects at all scales. This paper dis-cusses the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients from which solutions to the computing challenges of HEP can be formed. Beyond serving the community by ensuring that members are able to pursue research goals, this program serves individuals by providing intellectual capital and transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP
Rus, V. The Learner Data Institute—Conceptualization: A Progress Report. Retrieved from https://par.nsf.gov/biblio/10291239. Proceedings of the 2nd Learner Data Institute Workshop in Conjunction with The 14th International Educational Data Mining Conference .
Rus, V. The Learner Data Institute—Conceptualization: A Progress Report. Proceedings of the 2nd Learner Data Institute Workshop in Conjunction with The 14th International Educational Data Mining Conference, (). Retrieved from https://par.nsf.gov/biblio/10291239.
Rus, V.
"The Learner Data Institute—Conceptualization: A Progress Report". Proceedings of the 2nd Learner Data Institute Workshop in Conjunction with The 14th International Educational Data Mining Conference (). Country unknown/Code not available. https://par.nsf.gov/biblio/10291239.
@article{osti_10291239,
place = {Country unknown/Code not available},
title = {The Learner Data Institute—Conceptualization: A Progress Report},
url = {https://par.nsf.gov/biblio/10291239},
abstractNote = {This paper provides a progress report on the first 18 months of Phase 1, the conceptualization phase, of the Learner Data Institute (LDI; www.learnerdatainstitute.org). LDI is currently in Phase 1, the conceptualization phase, to be followed by Phase 2, the institute or convergence phase. The current 2-year conceptualization phase has two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering for the future institute, and (2) use the framework to provide prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. By targeting a critical mass of key challenges that are at a tipping point, LDI aims to start a chain reaction that will transform the whole learning ecosystem. We will emphasize here the key elements of the LDI science convergence framework that our team developed, implemented, and now is in the process of evaluating and refining. We highlight important outcomes of the convergence framework and related processes, including a 5-year plan for the institute phase and data-intensive prototype solutions to transform the learning ecosystem.},
journal = {Proceedings of the 2nd Learner Data Institute Workshop in Conjunction with The 14th International Educational Data Mining Conference},
author = {Rus, V.},
editor = {null}
}
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