Principles and a Framework of Performance Evaluation for Learners in Distance Vocational Education

AbstractAssessment is still a relatively weak component of distance vocational education, especially in the aspects of vocational skills. Performance evaluation pays more attention to the learner's development for skills, and therefore it is needed to introduce into distance vocational education. This paper puts forwards the principles of performance evaluation for learners in distance vocational education and multi-dimensional main bodies of evaluation. Based on those, the paper constructs an evaluation framework which includes learning attitude, learning behaviour, skills- acquisition, learning outcomes, and learning development

ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models

We study the domain adaptation problem with label shift in this work. Under the label shift context, the marginal distribution of the label varies across the training and testing datasets, while the conditional distribution of features given the label is the same. Traditional label shift adaptation methods either suffer from large estimation errors or require cumbersome post-prediction calibrations. To address these issues, we first propose a moment-matching framework for adapting the label shift based on the geometry of the influence function. Under such a framework, we propose a novel method named \underline{E}fficient \underline{L}abel \underline{S}hift \underline{A}daptation (ELSA), in which the adaptation weights can be estimated by solving linear systems. Theoretically, the ELSA estimator is $\sqrt{n}$-consistent ($n$ is the sample size of the source data) and asymptotically normal. Empirically, we show that ELSA can achieve state-of-the-art estimation performances without post-prediction calibrations, thus, gaining computational efficiency