A Design and Development of Rubrics System for Android Applications
- URL: http://arxiv.org/abs/2311.05628v1
- Date: Sat, 23 Sep 2023 16:14:27 GMT
- Title: A Design and Development of Rubrics System for Android Applications
- Authors: Kaustubh Kundu, Sushant Yadav, Tayyabbali Sayyad
- Abstract summary: This application aims to provide an user-friendly interface for viewing the students performance.
Our application promises to make the grading system easier and to enhance the effectiveness in terms of time and resources.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Online grading systems have become extremely prevalent as majority of
academic materials are in the process of being digitized, if not already done.
In this paper, we present the concept of design and implementation of a mobile
application for "Student Evaluation System", envisaged with the purpose of
making the task of evaluation of students performance by faculty and graders
facile. This application aims to provide an user-friendly interface for viewing
the students performance and has several functions which extends the Rubrics
with graphical analysis of students assignments. Rubrics evaluation system is
the widespread practice in both the software industry and the educational
institutes. Our application promises to make the grading system easier and to
enhance the effectiveness in terms of time and resources. This application also
allows the user/grader to keep track of submissions and the evaluated data in a
form that can be easily accessed and statistically analysed in a consistent
manner.
Related papers
- A Benchmark for Fairness-Aware Graph Learning [58.515305543487386]
We present an extensive benchmark on ten representative fairness-aware graph learning methods.
Our in-depth analysis reveals key insights into the strengths and limitations of existing methods.
arXiv Detail & Related papers (2024-07-16T18:43:43Z) - Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS)
GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
arXiv Detail & Related papers (2023-12-03T12:37:16Z) - Student Activity Recognition in Classroom Environments using Transfer
Learning [0.0]
This paper proposes a system for detecting and recognizing the activities of students in a classroom environment.
Xception achieved an accuracy of 93%, on the novel classroom dataset.
arXiv Detail & Related papers (2023-12-01T04:51:57Z) - Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs)
The system is into three inter-connected core processes-interaction, reflection, and reaction.
Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
arXiv Detail & Related papers (2023-09-15T02:42:03Z) - UX Heuristics and Checklist for Deep Learning powered Mobile
Applications with Image Classification [1.2437226707039446]
This study examines existing mobile applications with image classification and develops an initial set of AIXs for Deep Learning powered mobile applications with image classification decomposed into a checklist.
In order to facilitate the usage of the checklist we also developed an online course presenting the concepts and conductions as well as a web-based tool in order to support an evaluation using theses.
arXiv Detail & Related papers (2023-07-05T20:23:34Z) - Combining Gamification and Intelligent Tutoring Systems in a Serious
Game for Engineering Education [2.792030485253753]
We provide ongoing results from the development of a personalized learning system integrated into a serious game.
Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images.
We demonstrate the system's educational efficacy through pre-post-test results from students who played the game with and without the personalized learning system.
arXiv Detail & Related papers (2023-05-26T01:24:19Z) - A Machine Learning system to monitor student progress in educational
institutes [0.0]
We propose a data driven approach that makes use of Machine Learning techniques to generate a classifier called credit score.
The proposal to use credit score as progress indicator is well suited to be used in a Learning Management System.
arXiv Detail & Related papers (2022-11-02T08:24:08Z) - Retrieval-Enhanced Machine Learning [110.5237983180089]
We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases.
REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization.
REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
arXiv Detail & Related papers (2022-05-02T21:42:45Z) - Perspective on Code Submission and Automated Evaluation Platforms for
University Teaching [1.6172800007896284]
We present a perspective on platforms for code submission and automated evaluation in the context of university teaching.
We identify relevant technical and non-technical requirements for such platforms in terms of practical applicability and secure code submission environments.
We conclude that submission and automated evaluation involves continuous maintenance yet lowers the required workload for teachers and provides better evaluation transparency for students.
arXiv Detail & Related papers (2022-01-25T10:06:45Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z) - Empowering Active Learning to Jointly Optimize System and User Demands [70.66168547821019]
We propose a new active learning approach that jointly optimize the active learning system (training efficiently) and the user (receiving useful instances)
We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user.
We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
arXiv Detail & Related papers (2020-05-09T16:02:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.