Can an orchestration system increase collaborative, productive struggle in teaching-by-eliciting classrooms?

VanLehn, Kurt; Burkhardt, Hugh; Cheema, Salman; Kang, Seok-Min; Pead, Daniel; Schoenfeld, Alan H.; Wetzel, Jon

doi:10.1080/10494820.2019.1616567

Cited by 17 publications

(12 citation statements)

References 25 publications

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“…In addition to supporting improved student learning outcomes, the use of AI and ML in education has the potential to lead to improved teacher satisfaction (VanLehn et al 2019a , b ; Dietrich 2015 ). AI coupled with ML can provide 24/7 student support.…”

Section: Emerging Trends and Pedagogiesmentioning

confidence: 99%

Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education

August

Tsaima²

2021

SpringerBriefs in Statistics

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The role of artificial intelligence in US education is expanding. As education moves toward providing customized learning paths, the use of artificial intelligence (AI) and machine learning (ML) algorithms in learning systems increases. This can be viewed as growing metaphorical exoskeletons for instructors, enabling them to provide a higher level of guidance, feedback, and autonomy to learners. In turn, the instructor gains time to sense student needs and support authentic learning experiences that go beyond what AI and ML can provide. Applications of AI-based education technology support learning through automated tutoring, personalizing learning, assessing student knowledge, and automating tasks normally performed by the instructor. This technology raises questions about how it is best used, what data provides evidence of the impact of AI and ML on learning, and future directions in interactive learning systems. Exploration of the use of AI and ML for both co-curricular and independent learnings in content presentation and instruction; interactions, communications, and discussions; learner activities; assessment and evaluation; and co-curricular opportunities provide guidance for future research.

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Section: Emerging Trends and Pedagogiesmentioning

confidence: 99%

Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education

August

Tsaima²

2021

SpringerBriefs in Statistics

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“…TA systems have been developed across a range of disciplines, including HCI [11,12,66], learning analytics [83,86,127], teacher education [58,111,112], AI in education [40,55,125,141], and the learning sciences [71,122]. Their designs take various forms (e.g., see Figure 4), such as dashboard interfaces (e.g., [88,90,92,126]) peripheral information displays (e.g., [4,7,13,69,89]), or wearables (e.g., [19,55,58,103]).…”

Section: Related Work On Teaching Augmentationmentioning

confidence: 99%

“…These systems have been designed to provide support for a range of classroom contexts, from primary [13,129] to secondary [12,55] to higher [4,81] education, and from traditional [129] to blended [52] classrooms. Furthermore, TA tools have been designed to support a range of teacher processes, many of which are covered by the notion of classroom orchestration [30,71,125], such as monitoring [61,107,118,122] and decision-making [12,90,92,135]. TA tools have also been designed for teachers' professional development [37,93], such as real-time teacher coaching [58,112], or cultivating their on-the-spot reflectiveness [10,66].…”

Section: Related Work On Teaching Augmentationmentioning

confidence: 99%

“…Real-time learning analytics dashboards [88,90,118,127] are commonly designed to extend teachers' in-the-moment awareness in blended classrooms by presenting teachers with real-time information about students' progress and performance as they work with educational software [51,83,86,122,125]. This information is often displayed via lists, tables, or data visualizations (e.g., line plots and bar charts) on PCs or handheld mobile devices [2,25,41].…”

Section: Related Work On Teaching Augmentationmentioning

confidence: 99%

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The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms

Holstein

d'Anjou

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Figure 1. Two examples of real-time teaching augmentation. LEFT: ambient information on light objects supporting teachers' (a) division of time and attention over students [10] and (b) monitoring of computer-supported learning processes [12]. RIGHT: mixed reality glasses showing real-time indicators floating above students' heads, based on AI analytics, to direct teachers' attention towards students who may need it most [55].

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“…Both can help teachers perceive students' information in real time through a certain mechanism. FACT system is an intelligent classroom management system that can support students to solve mathematical problems collaboratively, and at the same time enhance teachers' perception and control ability [6]. Spinoza provides guidance and assistance to students and teachers by analyzing the real-time codes and operating behaviors of students in the process of programming (Nisenbaum et al, 2019) [7].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Promotion of a Multidimensional Information Intelligent Speech System in Dialect Teaching

Pang

2022

Journal of Sensors

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Due to the wide scale of learners, large individual differences and scattered distribution, dialect teaching is difficult to carry out effectively by traditional school education. In order to improve the teaching level of dialect, taking Cantonese as an example, this paper constructs a teaching evaluation system based on multidimensional information. Through the questionnaire and investigation of large Cantonese training institutions in Guangdong Province, the data set is formed, the CMA-ES algorithm with efficient optimization ability is selected to optimize the SVM, and the model is compared with ACO and SVM without optimization algorithm. Experimental results show that the average accuracy of CMA-ES algorithm is 95.85% and the average running time is 21.0 ms on 8 data sets, which has obvious advantages relatively. Based on the evaluation model, the basis for teaching optimization is found through sensitivity analysis, and student’s language expression is the most important index. And with the help of the intelligent voice system, the improvement measures for Cantonese teaching are proposed from the aspects of scene, oral, and scoring.

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Can an orchestration system increase collaborative, productive struggle in teaching-by-eliciting classrooms?

Cited by 17 publications

References 25 publications

Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education

Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education

The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms

Evaluation and Promotion of a Multidimensional Information Intelligent Speech System in Dialect Teaching

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