Reflective Writing About the Utility Value of Science as a Tool for Increasing STEM Motivation and Retention – Can AI Help Scale Up?

Klebanov, Beata Beigman; Burstein, Jill; Harackiewicz, Judith M.; Priniski, Stacy J.; Mulholland, M. Robert

doi:10.1007/s40593-017-0141-4

Cited by 31 publications

(15 citation statements)

References 32 publications

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“…This is consistent with experimental work showing that task value interventions increase engagement and conceptual change (Johnson & Sinatra 2013). Beigman Klebanov and colleagues (2017) analyzed these same essays using natural language processing techniques. They found that utility-value writing was characterized by argumentative and narrative elements, suggesting that students were both providing personal narratives and building arguments and claims(e.g., about why biology is valuable).Together, these analyses reveal some of the ways that writing about utility value can increase engagement and promote learning.…”

Section: Task Value Interventionsmentioning

confidence: 99%

“…Nearly all of the interventions in this review involve some amount of writing, and text analysis of students’ essays may offer new insights into how different groups internalize intervention messages and what types of writing interventions have the greatest benefits for students. Indeed, work by Harackiewicz and colleagues (2016a), Beigman Klebanov and colleagues (2017), Shnabel and colleagues (2013), and Tibbetts and colleagues (2016a) demonstrates that these words, direct from students’ pensor keyboards, can reveal underlying intervention mechanisms.…”

Section: Implications For Intervention Sciencementioning

confidence: 99%

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Improving Student Outcomes in Higher Education: The Science of Targeted Intervention

Harackiewicz

Priniski

2018

Annu. Rev. Psychol.

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Many theoretically based interventions have been developed over the past two decades to improve educational outcomes in higher education. Based in social-psychological and motivation theories, well-crafted interventions have proven remarkably effective because they target specific educational problems and the processes that underlie them. In this review, we evaluate the current state of the literature on targeted interventions in higher education with an eye to emerging theoretical and conceptual questions about intervention science. We review three types of interventions, which focus on the value students perceive in academic tasks, their framing of academic challenges, and their personal values, respectively. We consider interventions that (a) target academic outcomes (e.g., grades, major or career plans, course taking, retention) in higher education, as well as the pipeline to college, and (b) have been evaluated in at least two studies. Finally, we discuss implications for intervention science moving forward.

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Section: Task Value Interventionsmentioning

confidence: 99%

Section: Implications For Intervention Sciencementioning

confidence: 99%

Improving Student Outcomes in Higher Education: The Science of Targeted Intervention

Harackiewicz

Priniski

2018

Annu. Rev. Psychol.

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264

225

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“…The interplay between the use of artificial intelligence and its use in education has been observed to be engaging for learners [15] and is capable of educing (drawing out) AI-Thinking [16]. Educators also want to inculcate AI-Thinking [14,17] in learners, as AI has been shown to be beneficial in engaging learners to generate more questions when they discover the machine-learned hidden relationships among data variables [18].…”

Section: The Theoretical Basis Of Ai-thinkingmentioning

confidence: 99%

Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach

How

Chan

2020

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According to the World Health Organization (WHO) and the World Bank, malnutrition is one of the most serious but least-addressed development challenges in the world. Malnutrition refers to the malfunction or imbalance of nutrition, which could be influenced not only by under-nourishment, but also by over-nourishment. The significance of this paper is that it shows how artificial intelligence (AI) can be democratized to enable analysts who are not trained in computer science to also use human-centric explainable-AI to simulate the possible dynamics between malnutrition, health and population indicators in a dataset collected from 180 countries by the World Bank. This AI-based human-centric probabilistic reasoning approach can also be used as a cognitive scaffold to educe (draw out) AI-Thinking in analysts to ask further questions and gain deeper insights. In this study, a rudimentary beginner-friendly AI-based Bayesian predictive modeling approach was used to demonstrate how human-centric probabilistic reasoning could be utilized to analyze the dynamics of global malnutrition and optimize conditions for achieving the best-case scenario. Conditions of the worst-case “Black Swan” scenario were also simulated, and they could be used to inform stakeholders to prevent them from happening. Thus, the nutritional and health status of vulnerable populations could be ameliorated.

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“…Aggregate scores generated related to use of personal reflection language (Beigman Klebanov et al, 2017) complexnp phrasal complexity…”

Section: Pr1 Pr2 Personal Reflectionmentioning

confidence: 99%

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Tetreault

Burstein

Leacock³

et al. 2017

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Since the first workshop in 1997, BEA has become the leading venue for sharing and publishing innovative work that uses NLP to develop educational applications. The consistent interest and growth of the workshop has clear ties to challenges in education. The research presented at the workshop highlights advances in the technology and the maturity of the field of NLP in education. The capabilities serve as a response to educational challenges and are poised to support the needs of a variety of stakeholders, including educators, learners, parents, and administrators.NLP capabilities now support an array of learning domains, including writing, speaking, reading, and mathematics. In the writing and speech domains, automated writing evaluation (AWE) and speech assessment applications, respectively, are commercially deployed in high-stakes assessment and instructional settings, including Massive Open Online Courses (MOOCs). We also see widelyused commercial applications for plagiarism detection and peer review and explosive growth of mobile applications for game-based applications for instruction and assessment. The current educational and assessment landscape continues to foster a strong interest and high demand that pushes the state of the art in AWE capabilities to expand the analysis of written responses to writing genres other than those traditionally found in standardized assessments, especially writing tasks requiring use of sources and argumentative discourse.Steady growth in the development of NLP-based applications for education has prompted an increased number of workshops that typically focus on a single subfield. In BEA, we make an effort to have papers from many subfields, for example, tools for automated scoring, automated test-item generation, curriculum development, evaluation of text, dialogue, evaluation of genres beyond essays, feedback studies, and grammatical error correction.This year we received a record 62 submissions, and accepted 9 papers as oral presentations and 25 as poster presentation and/or demos, for an overall acceptance rate of 55 percent. Each paper was reviewed by three members of the Program Committee who were believed to be most appropriate for each paper. We continue to have a very strong policy to deal with conflicts of interest. First, we made a concerted effort to not assign papers to reviewers to evaluate if the paper had an author from their institution. Second, with respect to the organizing committee, authors of papers for which there was a conflict of interest recused themselves from the discussions.While the field is growing, we do recognize that there is a core group of institutions and researchers who work in this area. With a higher acceptance rate, we were able to include papers from a wider variety of topics and institutions. The papers accepted were selected on the basis of several factors, including the relevance to a core educational problem space, the novelty of the approach or domain, and the strength of the research. The accepted papers were highly diverse -...

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Reflective Writing About the Utility Value of Science as a Tool for Increasing STEM Motivation and Retention – Can AI Help Scale Up?

Cited by 31 publications

References 32 publications

Improving Student Outcomes in Higher Education: The Science of Targeted Intervention

Improving Student Outcomes in Higher Education: The Science of Targeted Intervention

Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

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