Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit

Traffic flow prediction is an important component of a modern intelligent transport system. Building an effective model for short term traffic flow prediction model is challenging. Traffic is spatial temporal in nature. A traffic flow prediction model should consider an appropriate scope of neighbourhood of traffic. To address the needs of a dynamic scope of neighbourhood. We introduce a novel gated recurrent unit variant call Neighbor Selecting Gated Recurrent Unit(NSGRU). NSGRU feature a learn-able spatial kernel with distance based K-nearest neighbor trimming scheme. Embedded external traffic knowledge are used to aid with the learning of spatial kernel. The NSGRU was evaluated with a quantized real world dataset and observed consistent improvement over baseline models

Looking through the fog of remote Zoom teaching: a case study of at-risk student prediction

Identification of students who are at-risk of failing or dropping out from a course is a key part of instructional remediation for student retention. The data-driven machine learning approach has proven to be effective in utilising student information to make the prediction. The Zoom video conferencing platform, which has become widely adopted to replace in-person teaching and learning in the COVID-19 pandemic, poses a challenge to building effective at-risk student prediction model. Extracting information about students is made difficult by increased capacity to control self-disclosure and the manipulation of online communication. The case study described in the paper aims to find out the feasibility of at-risk student prediction in Zoom teaching and the capacity of engineering informative features based on the polling function. A number of prediction scenarios were defined and the performance of the corresponding models and the effectiveness of various machine learning algorithm were evaluated. It was found that formative assessment features were useful for prediction scenarios earlier in the course, and summative assessment features gave accurate predictions towards the end. The findings have filled the knowledge gap of at-risk student prediction in Zoom teaching.</p

Automated short answer grading with computer-assisted grading example acquisition based on active learning

The technology of automated short answer grading (ASAG) can efficiently process answers according to human-prepared grading examples. Computer-assisted acquisition of grading examples uses a computer algorithm to sample real student responses for potentially good examples. The process is critical for optimizing the grading accuracy of machine learning models given a budget of human effort and the appeal of ASAG to online learning providers. This paper presents a novel method called short answer grading with active learning (SAGAL) that features a unified formulation comprising the heuristics for identifying potentially optimal examples of representative answers, borderline answers, and anomalous answers. The method is based on active learning, which iteratively samples good examples and queries for annotation to increase the sampling accuracy. SAGAL has been evaluated with three different public datasets of distinctive characteristics. The results show that the resulting models generally outperform the baseline semi-supervised learning methods on the same number of grading examples

A framework for effectively utilising human grading input in automated short answer grading

Short answer questions are effective for recall knowledge assessment. Grading a large amount of short answers is costly and time consuming. To apply short answer questions on MOOCs platforms, the issues of scalability and responsiveness must be addressed. Automated grading uses a computing process and a machine learning grading model to classify answers into correct, wrong, and other levels of correctness. The divide-and-grade approach is proven effective in reducing the annotation effort needed for the learning the grading model. This paper presents an improvement on the divide-and-grade approach that is designed to increase the utility of human actions. A novel short answer grading framework is proposed that addresses the selection of impactful answers for grading, the injection of the ground-truth grades for steering towards purer final clusters, and the final grade assignments. Experiment results indicate the grading quality can be improved with the same level of human actions

Mindfulness mitigates the adverse effects of problematic smartphone use on academic self-efficacy: A structural equation modelling analysis

While prior research has shown higher mindfulness is associated with lower problematic smartphone use (PSU), the contexts of these studies were not related to education or student performance. As such, whether and how mindfulness can reduce the adverse effects of PSU on academic self-efficacy remains unknown. This study proposed a model for testing whether and how mindfulness exerts its effects on PSU and academic self-efficacy through different pathways in which self-esteem, academic motivation, and smartphone time serve as mediators. To this end, a questionnaire survey, comprising Smartphone Addiction Scale (Short Version), Mindful Attention Awareness Scale, Short Academic Motivation Scale, Rosenberg Self-esteem Scale, and Educational Self-efficacy Scale, was administered to a sample of 821 university students from Hong Kong during the COVID-19 pandemic in 2022. The findings of this study show that mindfulness reduces the adverse effects of PSU on academic self-efficacy. Four different mediation pathways were identified, showing how the effects of mindfulness on PSU and academic self-efficacy are mediated through self-esteem, academic amotivation, and smartphone time. Specifically, mindfulness has a direct negative effect on PSU and a direct positive effect on academic self-efficacy. Mindfulness also exerts indirect effects on PSU mediated serially by self-esteem, academic amotivation, and smartphone time

An Interactive Short Answer Grading System based on Active Learning Models

Description to be added.Cannot be left empt