Education is a right of all, however, every individual is different than others. Teachers in post-communism era discover inherent individualism to equally train all towards job market of fourth industrial revolution. We can consider scenario of ethnic minority education in academic practices. Ethnic minority group has grown in their own culture and would prefer to be taught in their native way. We have formulated such linguistic anthropology(how people learn)based engagement as semi-supervised problem. Then, we have developed an conditional deep generative adversarial network algorithm namely LA-GAN to classify linguistic ethnographic features in student engagement. Theoretical justification proves the objective, regularization and loss function of our semi-supervised adversarial model. Survey questions are prepared to reach some form of assumptions about z-generation and ethnic minority group, whose learning style, learning approach and preference are our main area of interest.

翻译：教育是所有人的权利，然而每个个体都存在差异。后共产主义时代的教师发现，需要针对内在的个体差异性，平等地培养学生以使其适应第四次工业革命的就业市场。我们可以考虑学术实践中少数民族教育的场景。少数民族群体在其自身文化中成长，倾向于以本族方式接受教育。我们将这种基于语言人类学（即人类学习方式）的参与度问题建模为半监督问题。随后，我们开发了一种名为LA-GAN的条件深度生成对抗网络算法，用于对学生参与度中的语言民族志特征进行分类。理论论证证明了该半监督对抗模型的目标函数、正则化项和损失函数的合理性。我们设计了调查问卷，以对Z世代和少数民族群体的学习风格、学习方法和偏好（这些构成我们的主要研究兴趣）形成某种程度的假设。