International Journal of Smart Technology and Learning
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International Journal of Smart Technology and Learning (3 papers in press)
Smart Persuasive School Buildings: A state of the art by Coosje Hammink, Nienke Moor, Masi Mohammadi Abstract: Integrating technology in buildings can lead to smart buildings that can affect behavioural patterns. The increasing possibilities of ubiquitous computing and sensor technology can be employed to motivate behaviour change, particularly when embedded in the environment. One (smart) environment that can be particularly influential on daily and long-term behaviour is a school. An environment that aims at changing behaviour without forcing its inhabitants using smart technology, is what we call a smart persuasive environment. The potential these smart persuasive school buildings hold for influencing pupil behaviours, such as learning outcomes, social interaction and physical activity seems to be substantial. This article aims to give an overview of the state of the technology, the (effect on) behaviour of pupils and the theoretical mechanisms, using social cognitive theory, that could explain the relationship between smart persuasive school buildings and behaviour change. Keywords: Smart Persuasive School Buildings; Smart Learning Technology; Persuasive Technology; Systematic Literature Review; State of the art; Ambient Technology; Social Cognitive Theory.
Harness Big Data by iCycle - Intelligent Computer-supported Hybrid Collaborative Learning Environment by Hong Liu, Tyler Waner, Matthew Ikle, Shivansh Mittal Abstract: We present a collaborative learning environment called iCycle
(Intelligent Computer-supported hybrid Collaborative Learning Environment) along with enabling Artificial Intelligence (AI) applications. The learning environment developmental research is derived from funded research involving a long-term collaboration of four universities offering six cyberlearning courses in Computational Data-enabled Science and Engineering (CDSE). The CDSE courses emphasize using collaborative project-based learning (PBL) and data-driven learning assessment to promote deep-learning. Since offering the first two courses five years ago, we have collected almost a hundred Gigabytes of data from team projects and course materials. The relevant datasets from the collaboration may grow to Terabytes in the near future. These datasets are vital for evidence-based learning assessment and iterative course improvement. Since harnessing the big data manually is arduous and does not scale up, we developed AI applications to automate the data acquisition and analysis. The first AI application that we developed is an intelligent agent called BotCaptain for collecting teamwork data. The second AI application that we designed, but not yet implemented, is a system for formative learning assessment. Both AI applications use human-in-the-loop machine learning, and their automated services are currently limited to a few repetitive routines specified by instructors and teaching assistants. While AI technologies are routinely applied in business, their application to education is still at an early stage that we are just now learning to utilize its potential. In this paper, we demonstrate that AI can act as a powerful tool for supplemental instruction. To make the iCycle customizable to other PBL courses and serve large classes, we aim to keep improving its automation to reduce the instruction labor costs significantly.
Keywords: Computational Data-enabled Science and Engineering; Project Based Learning; Computer-Supported Collaborative Learning; Deep Learning Assessment; Teamwork.
3V-JMLE: A Three-valued Score Matrix-based Joint Parameter Estimation Algorithm in Computerized Adaptive Testing by Hong Zhou, Xiaoqing Gu, Jiale Zhou, Hongjiao Liu, Xiaoyu Yang Abstract: During the development of a computerized adaptive testing system (CAT), certain inadequacies were found in the popular joint maximum likelihood estimation (JMLE) algorithm utilized to acquire the ideal binary score matrix. When we attempted to acquire a questionresponse matrix from a traditional online test item pool system, more than 90% response elements were NA in the matrix. An ideal binary score matrix could scarcely be derived from it. Thus, we proposed a revised algorithm named three-valued JMLE (3V-JMLE) in this paper. When the response could not generate an ideal binary score matrix, we can transform the non-ideal binary matrix into a three-valued score matrix, and the parameters can be estimated simply by using 3V-JMLE. Experiment results show that 3V-JMLE has the same estimation accuracy and computational efficiency as JMLE. In addition, 3V-JMLE has an extensive range of applications and high testing efficiency. Keywords: Joint parameter estimation method; ideal binary score matrix; three-valued score matrix; computerized adaptive testing system; item response theory; logistic model.