ETHNO-DAANN: Ethnographic Engagement Classification by Deep Adversarial Transfer Learning

TL;DR

This paper introduces ETHNO-DAANN, a deep transfer learning model with adversarial adaptation designed to predict student engagement from ethnographic data, especially when labeled data is scarce, aiding educational reform and personalized learning.

Contribution

The paper presents a novel deep neural network approach that leverages transfer learning and adversarial adaptation for ethnographic engagement prediction with limited labeled data.

Findings

ETHNO-DAANN effectively predicts student engagement from ethnographic features.

The model identifies key ethnographic features influencing motivation.

Survey results highlight the most impactful ethnographic factors.

Abstract

Student motivation is a key research agenda due to the necessity of both postcolonial education reform and youth job-market adaptation in ongoing fourth industrial revolution. Post-communism era teachers are prompted to analyze student ethnicity information such as background, origin with the aim of providing better education. With the proliferation of smart-device data, ever-increasing demand for distance learning platforms and various survey results of virtual learning, we are fortunate to have some access to student engagement data. In this research, we are motivated to address the following questions: can we predict student engagement from ethnographic information when we have limited labeled knowledge? If the answer is yes, can we tell which features are most influential in ethnographic engagement learning? In this context, we have proposed a deep neural network based transfer learning algorithm ETHNO-DAANN with adversarial adaptation for ethnographic engagement prediction. We conduct a survey among participants about ethnicity-based student motivation to figure out the most influential feature helpful in final prediction. Thus, our research stands as a general solution for ethnographic motivation parameter estimation in case of limited labeled data.