AI Grand Challenges for Education

Woolf, Beverly Park; Lane, H. Chad; Chaudhri, Vinay K.; Kolodner, Janet L.

doi:10.1609/aimag.v34i4.2490

Cited by 113 publications

(69 citation statements)

References 43 publications

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“…Most of those studies have taken place in laboratory settings and involve awe induced by memory or association. A goal of educational psychology is to address how people learn in real world situations (Woolf, Woolf, Lane, Chaudhri & Kolodner, 2013). Museums are emotional places (May, Schlictmann, Paneto & Ducharme, 2018) with both experiential and learning goals.…”

Section: Resultsmentioning

confidence: 99%

Awe and Epistemology in a Science Museum

Price¹,

Quinn²,

Greenslit³

et al. 2019

Preprint

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Museums are located at the intersection of awe and learning: When guests arrive, they are expecting to be amazed, inspired, and educated. This is particularly true in science museums, and researchers have pointed to awe as an epistemic emotion that can promote science learning. We present two studies of awe in a science museum. The first study (n = 293) examined how aweconceptualized as a multifaceted construct associated with positive feelings of liberation/connection, negative feelings of oppression/isolation, chills, and diminished-self perceptions-differed across museum locations and in relation to prior knowledge. The second study (n = 708) expanded the investigation to also examine the relationship between awe and critical thinking. Across both studies, we found that, relative to baseline spaces, vast, beautiful spaces elicited positive awe-related emotions (amazement, curiosity) and stronger awe correlates (chills, diminished-self perceptions); vast, threatening spaces also elicited stronger amazement, chills, and diminished-self perceptions-but also stronger oppression/isolation. In both studies, pre-visit knowledge was associated with awe experiences during the visit. In Study 2, we also found evidence for relationships between awe and skepticism and aesthetic thought, two aspects of critical thinking: Positive aspects of awe (liberation/connection, curiosity) were associated with more skepticism, and negative aspects of awe (oppression/isolation, disorientation) with less skepticism. In terms of aesthetic thinking, diminished-self perceptions were associated being able describe observed roles and actions, and chills were associated with being able to describe personal opinions. These results support the assertion that awe can be used to encourage engagement and learning in informal science settings.

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Section: Resultsmentioning

confidence: 99%

Awe and Epistemology in a Science Museum

Price¹,

Quinn²,

Greenslit³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Important examples are intelligent-tutoring systems (ITS), mobile applications and simulation games built on foundations of machine learning, artificial intelligence, gaming and mobile app development, among other technologies. Such technologies could have the capability to enhance personalization, collaboration, engagement in and access to learning [15], while addressing educational challenges such as shortage of qualified teachers and lack of innovative educational materials, especially with their current availability in developing countries, both home-grown (developed by local researchers) and designed elsewhere [14]. Below are some specific examples of successful educational technology implementations in alert and warning countries, identified through reviews of educational technology literature, that could inspire adaptive learning system development and adoption for EET in the fragile context.…”

Section: Tackling the Challenges: The Promise Of Technologymentioning

confidence: 99%

“…Adaptive learning technologies could provide significant value in less-developed countries by supporting and supplementing their educational needs [14], while enabling personalization of learning, establishing connections between learners, increasing student engagement, and providing access to various learning materials [15]. With even the world's least-developed countries currently exhibiting rapid growth in technology adoption [16], there is little reason digitalization cannot become a friend of, rather than a threat to, the advancement of global sustainable development.…”

Section: Introductionmentioning

confidence: 99%

Entrepreneurship Education and Sustainable Development Goals: A literature Review and a Closer Look at Fragile States and Technology-Enabled Approaches

Rashid

2019

Sustainability

120

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Entrepreneurship has the potential to reduce poverty, stimulate economic growth and boost innovation, in addition to enhancing social and environmental sustainability. In accordance with the human capital theory and previous empirical studies, it is assumed that entrepreneurship education and training (EET) directly correlates with positive entrepreneurial outcomes and therefore sustainable development. Although several scholars have attempted to review and analyze EET literature over the past decade, none of these reviews directly links EET with sustainable development or focuses on the role and status of EET (research) in less-stable areas of the world. This systematic review thus attempts to analyze recent literature to identify the extent to which EET research addresses Sustainable Development Goals (SDGs). The review identifies several gaps in research and practice that potentially hinder EET from adequately advancing sustainable development, including a dearth of research on fragile states and demographic diversity, limited EET access to non-university students and a general lack of focus on educational technology, progressive education approaches, and innovation in fragile countries compared to stable ones. The review also identifies challenges pertaining to EET resource constraints in fragile contexts. The paper concludes by offering insights on how educational technology could mitigate EET challenges in fragile environments to ultimately ease some barriers towards SDG advancement and provides recommendations for future research directions.

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“…Commonsense knowledge (CSK) in machine intelligence was proposed to discuss open issues of CSK acquisition, CSK in natural language, and applications of CSK [38]. Woolf et al [39] discussed the future challenges of AI in education. Xu et al [40] proposed a machine learning method to track and predict student performance in degree programs.…”

Section: Artificial Intelligence and Learningmentioning

confidence: 99%

An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

Su¹,

Liao²,

Chen³

2019

Applied Sciences

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For a student, determining how to choose from a set of courses is an important issue prior to learning. An appropriate learning guide can direct students toward an area of interest. The learning results produced by the student in this case are superior due to their strong interest in the subject matter. Although a number of methods have been proposed to address this issue, the effectiveness remains unsatisfactory. To this end, we created an effective system, called the personality-driven course decision assistant, to help students determine the courses they should select by mining and filtering learners’ personality patterns. For learner pattern mining, the relationships between the students’ learning results and the referred personalities are discovered to provide the learners with valuable information before learning. For filtering learner personality patterns, students with similar personality patterns are filtered to predict the potential learning results. Through the actual system, a number of subjective and objective evaluations were conducted, and the evaluation results reveal that the proposed system is highly effective and reliable.

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AI Grand Challenges for Education

Cited by 113 publications

References 43 publications

Awe and Epistemology in a Science Museum

Awe and Epistemology in a Science Museum

Entrepreneurship Education and Sustainable Development Goals: A literature Review and a Closer Look at Fragile States and Technology-Enabled Approaches

An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

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