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Student motivation is a key research agenda due to the necessity of bothpostcolonial education reform and youth job-market adaptation in ongoing fourthindustrial revolution. Post-communism era teachers are prompted to analyzestudent ethnicity information such as background, origin with the aim ofproviding better education. With the proliferation of smart-device data,ever-increasing demand for distance learning platforms and various surveyresults of virtual learning, we are fortunate to have some access to studentengagement data. In this research, we are motivated to address the followingquestions: can we predict student engagement from ethnographic information whenwe have limited labeled knowledge? If the answer is yes, can we tell whichfeatures are most influential in ethnographic engagement learning? In thiscontext, we have proposed a deep neural network based transfer learningalgorithm ETHNO-DAANN with adversarial adaptation for ethnographic engagementprediction. We conduct a survey among participants about ethnicity-basedstudent motivation to figure out the most influential feature helpful in finalprediction. Thus, our research stands as a general solution for ethnographicmotivation parameter estimation in case of limited labeled data.

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