Annete: An Intelligent Tutoring Companion Embedded into the Eclipse IDE

Day, Melissa; Penumala, Manohara Rao; Gonzalez-Sanchez, Javier

doi:10.1109/cogmi48466.2019.00018

Cited by 13 publications

(2 citation statements)

References 19 publications

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“…The setup depicts the VANETs that adopt one routing protocol at a time for communication and the rest of the other parameters. These parameters are configured in the ONE simulator source code obtained [49], [48] compiled and run with the Eclipse Integrated Development Environment (IDE) [51], [52], to obtain results which are compared with the results obtained from the algorithm setup proposed.…”

Section: E One Simulation Setupmentioning

confidence: 99%

Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks

Nakayima,

Soliman,

Ueda

et al. 2024

IEEE Open J. Veh. Technol.

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Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.

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Section: E One Simulation Setupmentioning

confidence: 99%

Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks

Nakayima,

Soliman,

Ueda

et al. 2024

IEEE Open J. Veh. Technol.

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“…Day et al [104] introduced Annete, an intelligent tutor for Eclipse, to give feedback on correctness and readability. They use a neural network with a supervised learning algorithm to determine if a student needs assistance with their code and determines what feedback should be shown.…”

Section: Documentationmentioning

confidence: 99%

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

Messer,

Brown,

Kölling

et al. 2024

ACM Trans. Comput. Educ.

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We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques’ feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools’ performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.

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Designing a Gender-Inclusive Conversational Agent For Pair Programming: An Empirical Investigation

Kuttal

Sedhain

AuBuchon

2021

Artificial Intelligence in HCI

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Annete: An Intelligent Tutoring Companion Embedded into the Eclipse IDE

Cited by 13 publications

References 19 publications

Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks

Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

Designing a Gender-Inclusive Conversational Agent For Pair Programming: An Empirical Investigation

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