A priority of preferences for teacher assignment problem

Overview

Genetic-Algorithm-for-Assignment-Problem

A priority of preferences for teacher assignment problem

Keywords

k-partition; clustering; education 4.0

Abstract

According to the credit training regulations, students will have to create their own learning path to complete the training program and must actively register for subjects in each of their semesters. To prepare for the student’s course registration, usually at the beginning of each semester, the training department and the head of the department will work together to carry out the following steps: (1) based on the program’s reference road map and individual study plan of students in the upcoming semester, the training department counts the number of classes that need to be opened for each subject (referred to as subject classes) and sends a list of these subject classes to the head of the department, (2) the head of the department assigns the permanent faculty members of the department to be in charge of teaching these subject classes, if not enough, additional visiting lecturers must be invited outside the school, (3) based on the assignment proposed by the head of the department, the training department arranges the timetable for these subject classes, (4) students register for the course on this schedule, then the training department and the head of the department will make adjustments (if any) to suit the student’s actual course registration situation such as opening more subject classes or canceling subject classes, (5) after the final schedule, instruction for that semester will take place.

Currently, in Step 2, the head of the department assigns full-time lecturers in charge of teaching classes difficultly and emotionally. The difficulty is because the number of subject classes is large, as well as the number of full-time lecturers in the subject, and the expertise of the full-time lecturers is very different. The emotionality is because at present there is only one measurable criterion, which is to assign full-time lecturers in charge of subject classes as much as possible so as not to have to invite additional visiting lecturers outside the school. However, the criteria of teacher satisfaction have not been clearly paid attention to.

In this study, the authors propose a quantitative way of teachers’ satisfaction by collecting their interest in subjects through priority for each subject and their maximum teaching capacity in each semester. In fact, some teachers like to teach their familiar subjects, while others like to teach new subjects. Therefore, in this

Related Work

You might also like...
Code for our TKDE paper "Understanding WeChat User Preferences and “Wow” Diffusion"

wechat-wow-analysis Understanding WeChat User Preferences and “Wow” Diffusion. Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang,

Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

This is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.

 PREFS is a Python library to store and manage preferences and settings.
PREFS is a Python library to store and manage preferences and settings.

PREFS PREFS is a Python library to store and manage preferences and settings. PREFS stores a Python dictionary in a total human-readable file, the PRE

Inferring Lexicographically-Ordered Rewards from Preferences

Inferring Lexicographically-Ordered Rewards from Preferences Code author: Alihan Hüyük ([email protected]) This repository contains the source code nec

Negative sampling for solving the unlabeled entity problem in NER. ICLR-2021 paper: Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition.

Negative Sampling for NER Unlabeled entity problem is prevalent in many NER scenarios (e.g., weakly supervised NER). Our paper in ICLR-2021 proposes u

Problem-943.-ACMP - Problem 943. ACMP
Problem-943.-ACMP - Problem 943. ACMP

Problem-943.-ACMP В "main.py" расположен вариант моего решения задачи 943 с серв

Implementation of momentum^2 teacher

Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning Requirements All experiments are done with python3.6, torch

PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Unbiased Teacher for Semi-Supervised Object Detection This is the PyTorch implementation of our paper: Unbiased Teacher for Semi-Supervised Object Detection

[ICLR 2021 Spotlight Oral] "Undistillable: Making A Nasty Teacher That CANNOT teach students", Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang

Undistillable: Making A Nasty Teacher That CANNOT teach students "Undistillable: Making A Nasty Teacher That CANNOT teach students" Haoyu Ma, Tianlong

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation
AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.
This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

Unet network with mean teacher for altrasound image segmentation

Unet network with mean teacher for altrasound image segmentation

Example teacher bot for deployment to Chai app.
Example teacher bot for deployment to Chai app.

Create and share your own chatbot Here is the code for uploading the popular "Ms Harris (Teacher)" chatbot to the Chai app. You can tweak the config t

This repo provides the source code for
This repo provides the source code for "Cross-Domain Adaptive Teacher for Object Detection".

Cross-Domain Adaptive Teacher for Object Detection This is the PyTorch implementation of our paper: Cross-Domain Adaptive Teacher for Object Detection

An assignment on creating a minimalist neural network toolkit for CS11-747

minnn by Graham Neubig, Zhisong Zhang, and Divyansh Kaushik This is an exercise in developing a minimalist neural network toolkit for NLP, part of Car

Minimalist BERT implementation assignment for CS11-747

minbert Assignment by Zhengbao Jiang, Shuyan Zhou, and Ritam Dutt This is an exercise in developing a minimalist version of BERT, part of Carnegie Mel

Official implementation of our paper
Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection This project provides an implementation for "LLA: Loss-aware Label Assignment for Dens

Owner
hades
Machine Learning, Deep Learning, Computer Vision, Signal processing
hades
Parameterising Simulated Annealing for the Travelling Salesman Problem

Parameterising Simulated Annealing for the Travelling Salesman Problem Abstract The Travelling Salesman Problem is a well known NP-Hard problem. Given

Gary Sun 55 Jun 15, 2022
Algorithm for Cutting Stock Problem using Google OR-Tools. Link to the tool:

Cutting Stock Problem Cutting Stock Problem (CSP) deals with planning the cutting of items (rods / sheets) from given stock items (which are usually o

Emad Ehsan 87 Dec 31, 2022
A Python program to easily solve the n-queens problem using min-conflicts algorithm

QueensProblem A program to easily solve the n-queens problem using min-conflicts algorithm Performances estimated with a sample of 1000 different rand

null 0 Oct 21, 2022
N Queen Problem using Genetic Algorithm

The N Queen is the problem of placing N chess queens on an N×N chessboard so that no two queens attack each other.

Mahdi Hassanzadeh 2 Nov 11, 2022
Using A * search algorithm and GBFS search algorithm to solve the Romanian problem

Romanian-problem-using-Astar-and-GBFS Using A * search algorithm and GBFS search algorithm to solve the Romanian problem Romanian problem: The agent i

Mahdi Hassanzadeh 6 Nov 22, 2022
Session-based Recommendation, CoHHN, price preferences, interest preferences, Heterogeneous Hypergraph, Co-guided Learning, SIGIR2022

This is our implementation for the paper: Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation Xiaokun Zhang, Bo

Xiaokun Zhang 27 Dec 2, 2022
This is the official pytorch implementation of Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation(TESKD)

Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation (TESKD) By Zheng Li[1,4], Xiang Li[2], Lingfeng Yang[2,4], Jian Yang[2], Zh

Zheng Li 9 Sep 26, 2022
Application for easy configuration of swap file and swappiness priority in slackware and others linux distributions.

Swap File Program created with the objective of assisting in the configuration of swap file in Distributions such as Slackware. Required packages: pyt

Mauricio Ferrari 3 Aug 6, 2022
2b2t Priority queue discord bot announcer

2b2t Priority queue discord bot announcer Commands !prioq -> Checks the priority queue length and sends it. !start -> Starts a loop that sends the sta

Gumi 5 Jun 6, 2022
Task-manager-CLI with Priority Modification

Task-manager-CLI with Priority Modification The functions for the app have been written in task.py file. 1. Install Node.js This project requires Node

null 1 Jan 21, 2022