🗒️学习工程learning engineering
2023-11-26
| 2023-11-26
0  |  0 分钟
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今天准备写在线学习的教材。原来的《现代远程教育》已经不能在写了，写《在线教育的理论与实践》已经有好几本了，所以想着是不是写《在线学习设计与研究》。结果兜兜转转看到了learning engineering。顺便记一下对这个词的探究。
 

📝 什么是学习工程

学习技术系统地应用教育技术和学习科学的循证原则和方法来创造引人入胜和有效的学习体验，并支持学习者的学习困难和挑战。

观点1

学习工程旨在通过利用计算来显著提高学习科学作为一门学科的应用和有效性，从而改善教育成果。

观点2

学习工程是使用以人为本的工程设计方法和数据驱动的决策来应用学习科学以支持学习者及其发展的过程

🤗 学习工程发展的10个可能

https://tmb.apaopen.org/pub/5ib9cpqa#nncdnbp6cx3
Top 10 opportunities
Examples
Better Learning Engineering
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1. Enhance R&D Infrastructure in Widely Deployed Platforms
Make high-quality data available for a broader range of platforms Develop an ecosystem where researchers can more easily build on each others’ findings and research code Develop general purpose software components for identifying how effective content is Extend experimentation infrastructure to a broader range of learning platforms, along with good tools for authoring content for studies Extend experimentation testing infrastructure to study the effectiveness of combined interventions Develop general purpose software components for reinforcement learning Embed measures of student affect, self-regulated learning, and engagement into learning engineering platforms Support the development of easier processes and technologies for IRB and privacy compliance for learning platforms
2. Build Components to Create Next-Generation Learning Technologies Faster
Create production-grade components for student modeling that can be integrated into different learning systems and used at scale Support research on the data needs and practical limitations of modern student modeling algorithms Create reusable components for interventions such as mindset interventions Develop production-grade toolkits to facilitate modeling complex student behavior Develop toolkits for natural language processing in education
3. Learning Engineering to Support Diversity and Enhance Equity
Require that projects collect more complete data on learner identity and characteristics Require that projects check models and findings for algorithmic bias and differential impact Encourage participatory and inclusive design, involving members of the communities impacted
4. Bring Learning Engineering to Domain-Based Educational Research
Create a network to incentivize and scaffold widespread sharing and collaboration on domain knowledge gaps Fund support for hybrid AI/human methods for knowledge graph discovery Support infrastructure for discovering and remedying student misconceptions
Support human processes
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5. Enhance Human–Computer Systems
Increase the richness of data given to teachers while maintaining usability and comprehensibility Provide teachers with real-time recommendations about when to provide additional support to students and what kind of support to provide Support research on integration of computer tutoring and human tutoring
6. Better Engineer Learning System Implementation in Schools
Improve integration of data between classroom practices, students’ learning experiences, and teacher professional development to study which practices around the use of learning technology are effective and scalable Develop a taxonomy of teacher practices around the use of learning technology, and use it to study which practices and professional development is effective and scalable Develop automated and semiautomated methods to encourage teachers to use the right practice at the right time
7. Improve Recommendation, Assignment, and Advising Systems
Develop advising and recommendation systems that support better advising practices Design explainable AI methods for repurposing prediction models into easy-to-understand recommendations for advisors and students Fund infrastructure that enables experimentation around prediction and recommendation and connects it with outcome data
Better learning technologies
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8. Optimize for Robust Learning and Long-Term Achievement
Increase awareness of existing cognitive science findings around robust learning Incentivize and plan for longer-term follow-up for A/B studies
9. Support Learning 21st-Century Skills and Collaboration
Develop data science challenges to drive competition to create reliable and valid measures of 21st-century skills, including collaboration, using new technologies and data collection methods Develop data science challenges to drive competition to create learning systems that scaffold collaboration and support the development of 21st-century skills
10. Improved Support for Student Engagement
Examine which engagement/affective interventions (both teacher-driven and automated) are effective for which students, in which situations Create a competition where engagement/affective interventions are combined and compared in a sample large enough to also study individual differences Develop better understanding of teacher and student preferences and comfort for engagement/affective interventions
Note. R&D = research and development; AI = artificial intelligence; IRB = Institutional Review Board
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📎 参考文章

  • BAKER R S, BOSER U, SNOW E L. 2022. Learning Engineering: A View on Where the Field Is at, Where It’s Going, and the Research Needed[J/OL]. Technology, Mind, and Behavior, 3(1: Spring 2022)[2023-11-26]. https://tmb.apaopen.org/pub/5ib9cpqa/release/1.
 
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