Reinforcement Learning for the Adaptive Scheduling of Educational Activities

Bassen, Jonathan; Balaji, Bharathan; Schaarschmidt, Michael; Thille, Candace; Painter, J. D.; Zimmaro, Dawn; Games, Alex; Fast, Ethan; Mitchell, John C.

doi:10.1145/3313831.3376518

Cited by 45 publications

(37 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dove et al [209] surveyed to understand how design innovation is practiced in the ML domain in terms of user experience. [63], [70], [165], [174], [211] [124], [125], [128], [175], [217] [69], [112], [113], [118], [130], [131], [133], [135], [137], [140], [144], [152], [153], [155] [49], [57], [67], [98], [110], [114], [180], [192], [193], [203], [214], [215] [48], [59], [62], [75], [79], [111], [120], [156], [159], [185], [195], [197], [207], [218] [65], [83],…”

Section: Survey Resultsmentioning

confidence: 99%

“…Pfau et al [ 174 ] looked at how Deep Learning techniques can improve dynamic difficulty adjustments in games. Bassen et al [ 175 ] developed a reinforcement learning algorithm to optimize educational activities in online courses. Donkers et al [ 176 ] developed a recommendation and explanation method and evaluated the quality with user studies.…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

View full text Add to dashboard Cite

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

show abstract

Section: Survey Resultsmentioning

confidence: 99%

Section: Classifying Hcml Researchmentioning

confidence: 99%

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Multisample RL [14] can adapt to humans and changes in their response times under various autonomous actions. Amazon has used personalized reinforcement learning to adapt to students' preferences [5]. Moreover, RL models have been used to decide residential load scheduling [55].…”

Section: Related Workmentioning

confidence: 99%

FaiR-IoT

Elmalaki

2021

Proceedings of the International Conference on Internet-of-Things Design and Implementation

View full text Add to dashboard Cite

Thanks to the rapid growth in wearable technologies, monitoring complex human context becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing such personalized IoT applications arises from human variability. Such variability stems from the fact that different humans exhibit different behaviors when interacting with IoT applications (intra-human variability), the same human may change the behavior over time when interacting with the same IoT application (inter-human variability), and human behavior may be affected by the behaviors of other people in the same environment (multi-human variability). To that end, we propose FaiR-IoT, a general reinforcement learning-based framework for adaptive and fairness-aware human-in-the-loop IoT applications. In FaiR-IoT, three levels of reinforcement learning agents interact to continuously learn human preferences and maximize the system's performance and fairness while taking into account the intra-, inter-, and multi-human variability. We validate the proposed framework on two applications, namely (i) Human-in-the-Loop Automotive Advanced Driver Assistance Systems and (ii) Human-in-the-Loop Smart House. Results obtained on these two applications validate the generality of FaiR-IoT and its ability to provide a personalized experience while enhancing the system's performance by 40%−60% compared to non-personalized systems and enhancing the fairness of the multi-human systems by 1.5 orders of magnitude. CCS Concepts• Computing methodologies → Multi-agent reinforcement learning; • Human-centered computing → Ubiquitous computing; Collaborative and social computing.

show abstract

“…For several decades, scheduling and job scheduling have received constant attention from both researchers and practitioners from across the world. Such a level of interest is justified by the broad applications of scheduling which go from production planning and manufacturing [1,2], construction [3], cloud computing [4,5], transportation [6], education [7], healthcare [8] to CPUs [9], Internet of Things [10] and sensor networks [11]. Moreover, many scheduling problems are NP-hard, increasing the importance of developing faster and more efficient solving methods for scheduling problems with real-life instance sizes.…”

Section: Introductionmentioning

confidence: 99%

Parallel Makespan Calculation for Flow Shop Scheduling Problem with Minimal and Maximal Idle Time

Rudy

2021

Applied Sciences

View full text Add to dashboard Cite

In this paper, a flow shop scheduling problem with minimal and maximal machine idle time with the goal of minimizing makespan is considered. The mathematical model of the problem is presented. A generalization of the prefix sum, called the job shift scan, for computing required shifts for overlapping jobs is proposed. A work-efficient algorithm for computing the job shift scan in parallel for the PRAM model with n processors is proposed and its time complexity of O(logn) is proven. Then, an algorithm for computing the makespan in time O(mlogn) in parallel using the prefix sum and job shift scan is proposed. Computer experiments on GPU were conducted using the CUDA platform. The results indicate multi-thread GPU vs. single-thread GPU speedups of up to 350 and 1000 for job shift scan and makespan calculation algorithms, respectively. Multi-thread GPU vs. single-thread CPU speedups up to 4.5 and 14.7, respectively, were observed as well. The experiments on the Taillard-based problem instances using a simulated annealing solving method and employing the parallel makespan calculation show that the method is able to perform many more iterations in the given time limit and obtain better results than the non-parallel version.

show abstract

Reinforcement Learning for the Adaptive Scheduling of Educational Activities

Cited by 45 publications

References 15 publications

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

FaiR-IoT

Parallel Makespan Calculation for Flow Shop Scheduling Problem with Minimal and Maximal Idle Time

Contact Info

Product

Resources

About