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Technological developments have spawned a range of educational software that strives to enhance learning through personalized adaptation. The success of these systems depends on how accurate the knowledge state of individual learners is modeled over time. Computer scientists have been at the forefront of development for these kinds of distributed learning systems and have primarily relied on data-driven algorithms to trace knowledge acquisition in noisy and complex learning domains. Meanwhile, research psychologists have primarily relied on data collected in controlled laboratory settings to develop and validate theory-driven computational models, but have not devoted much exploration to learning in naturalistic environments. The two fields have largely operated in parallel despite considerable overlap in goals. We argue that mutual benefits would result from identifying and implementing more accurate methods to model the temporal dynamics of learning and forgetting for individual learners. Here we discuss recent efforts in developing adaptive learning technologies to highlight the strengths and weaknesses inherent in the typical approaches of both fields. We argue that a closer collaboration between the educational machine learning/data mining and cognitive psychology communities would be a productive and exciting direction for adaptive learning system application to move in.
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Critical zone science in the Western US—Too much information?
Exponentially growing publication rates are increasingly problematic for interdisciplinary fields like Critical Zone (CZ) science. How does one “keep up” across different, but related fields with unique hypotheses, field techniques, and models? By surveying CZ academics in the Western US, a region with substantial CZ research, we document the challenge. While conventional knowledge synthesis products-particularly review papers clearly support knowledge transfer, they are static and limited in scope. More informal paths for knowledge transfer, including social networking at conferences and academic mentorship, are useful but are unstructured and problematic for young scientists or others who may not have access to these resources. While new machine-learning tools, including ChatGPT, offer new ways forward for knowledge synthesis, we argue that they do not necessarily solve the problem of information overload in CZ Science. Instead, we argue that what we need is a community driven, machine aided knowledge tool that evolves and connects, but preserves the richness of detail found in peer-reviewed papers. The platform would be designed by CZ scientists, machine-aided and built on the strengths of people-driven synthesis. By involving the scientist in the design of this tool, it will better reflect the practice of CZ science-including hypothesis generation, testing across different time and space scales and in different time periods and locations, and, importantly, the use and evaluation of multiple, often sophisticated methods including fieldwork, remote sensing, and modeling. We seek a platform design that increases the findability and accessibility of current working knowledge while communicating the CZ science practice.
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- Award ID(s):
- 2012821
- PAR ID:
- 10523768
- Publisher / Repository:
- Frontiers in Water
- Date Published:
- Journal Name:
- Frontiers in Water
- Volume:
- 5
- Issue:
- 1226612
- ISSN:
- 2624-9375
- Subject(s) / Keyword(s):
- critical zone science, AI, publishing, cross-discplinary
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frwa.2023.1226612
