Conditional Teacher-Student Learning

The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance. To overcome this problem, we propose a conditional T/S learning scheme, in which a "smart" student model selectively chooses to learn from either the teacher model or the ground truth labels conditioned on whether the teacher can correctly predict the ground truth. Unlike a naive linear combination of the two knowledge sources, the conditional learning is exclusively engaged with the teacher model when the teacher model's prediction is correct, and otherwise backs off to the ground truth. Thus, the student model is able to learn effectively from the teacher and even potentially surpass the teacher. We examine the proposed learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker adaptation on Microsoft short message dictation dataset. The proposed method achieves 9.8% and 12.8% relative word error rate reductions, respectively, over T/S learning for environment adaptation and speaker-independent model for speaker adaptation.Comment: 5 pages, 1 figure, ICASSP 201

Enhanced Multimodal Representation Learning with Cross-modal KD

This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD). The widely adopted mutual information maximization-based objective leads to a short-cut solution of the weak teacher, i.e., achieving the maximum mutual information by simply making the teacher model as weak as the student model. To prevent such a weak solution, we introduce an additional objective term, i.e., the mutual information between the teacher and the auxiliary modality model. Besides, to narrow down the information gap between the student and teacher, we further propose to minimize the conditional entropy of the teacher given the student. Novel training schemes based on contrastive learning and adversarial learning are designed to optimize the mutual information and the conditional entropy, respectively. Experimental results on three popular multimodal benchmark datasets have shown that the proposed method outperforms a range of state-of-the-art approaches for video recognition, video retrieval and emotion classification.Comment: Accepted by CVPR202

Design Principles to support Student Learning in Teacher Learning Groups

This study presents design principles for student facilitation in teacher learning groups (TLGs), based on a systematic literature review searching for characteristics, conditions, and outcomes of students working in TLGs. Notions of team learning, network learning, community learning, and collective learning within teacher education were taken as the main components of the search. The review turned out to be very lean in terms of input; only 17 articles did justice to this theme. The exercise resulted in five main characteristics of TLGs (i.e. shared vision and goals; a project-based approach; shared responsibility and ownership; diversity and equality; supportive structures, resources and roles) and associated conditional factors. We combined these characteristics and conditional factors to formulate design principles, which can serve as a starting point for the supervision of students in TLGs. The limited number of search results shows that more research into student learning in TLGs is needed. Furthermore, the design principles yielded by the review are formulated in very general terms. In follow-up research, we will monitor four institutes for primary teacher education that enable student learning in TLGs with various social configurations. This study is expected to further concretize the design principles for student learning in TLGs

Short-run learning dynamics under a test-based accountability system : evidence from Pakistan

Low student learning is a common finding in much of the developing world. This paper uses a relatively unique dataset of five semiannual rounds of standardized test data to characterize and explain the short-term changes in student learning. The data are collected as part of the quality assurance system for a public-private partnership program that offers public subsidies conditional on minimum learning levels to low-cost private schools in Pakistan. Apart from a large positive distributional shift in learning between the first two test rounds, the learning distributions over test rounds show little progress. Schools are ejected from the program if they fail to achieve a minimum pass rate in the test in two consecutive attempts, making the test high stakes. Sharp regression discontinuity estimates show that the threat of program exit on schools that barely failed the test for the first time induces large learning gains. The large change in learning between the first two test rounds is likely attributable to this accountability pressure given that a large share of new program entrants failed in the first test round. Schools also qualify for substantial annual teacher bonuses if they achieve a minimum score in a composite measure of student test participation and mean test score. Sharp regression discontinuity estimates do not show that the prospect of future teacher bonus rewards induces learning gains for schools that barely did not qualify for the bonus.Tertiary Education,Education For All,Secondary Education,Teaching and Learning,Primary Education

Is student procrastination related to controlling teacher behaviours?

[EN] Even motivated students procrastinate, for procrastination is triggered by a volitional (rather than by a motivational) problem. However, many factors, such as learning context, teacher interpersonal style, and also type of motivation may influence the occurrence of procrastination. The aim of the present study was to assess the relations between first-year university students’ procrastination and controlling teacher behaviour. Four types of controlling teacher behaviour and three distinct measures of procrastination were ecvaluated and their correlations assessed. Findings revealed small but significant associations between (a) conditional use of rewards and decisional procrastination, and between (b) excessive personal control and procrastination linked to avoiding tasks. Results suggest that controlling teacher behaviours might influence students’ psychological experiences in learning negatively. Teachers who do not refrain from constant use of conditional rewards may deffer students’ decision processes regarding their own autonomous academic learning, and excessive personal control may favour students’ perceptions of external regulations, decreasing intrinsic motivation and autonomous self-regulated learning and, thus, making it more likely to engage in alternative activities, procrastinating academic learning.Valenzuela, R.; Codina, N.; Pestana, J.; González-Conde, J. (2017). Is student procrastination related to controlling teacher behaviours?. En Proceedings of the 3rd International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 1130-1137. https://doi.org/10.4995/HEAD17.2017.5530OCS1130113

Short-run Learning Dynamics under a Test-based Accountability System: Evidence from Pakistan

Low student learning is a common finding in much of the developing world. This paper uses a relatively unique dataset of five semiannual rounds of standardized test data to characterize and explain the short-term changes in student learning. The data are collected as part of the quality assurance system for a public-private partnership program that offers public subsidies conditional on minimum learning levels to low-cost private schools in Pakistan. Apart from a large positive distributional shift in learning between the first two test rounds, the learning distributions over test rounds show little progress. Schools are ejected from the program if they fail to achieve a minimum pass rate in the test in two consecutive attempts, making the test high stakes. Sharp regression discontinuity estimates show that the threat of program exit on schools that barely failed the test for the first time induces large learning gains. The large change in learning between the first two test rounds is likely attributable to this accountability pressure given that a large share of new program entrants failed in the first test round. Schools also qualify for substantial annual teacher bonuses if they achieve a minimum score in a composite measure of student test participation and mean test score. Sharp regression discontinuity estimates do not show that the prospect of future teacher bonus rewards induces learning gains for schools that barely did not qualify for the bonus

Evaluating service supply in conditional cash transfers

textConditional cash transfers are poverty reduction mechanisms that seek to increase demand of social services by combining an income effect with a health or education requirement. This demand-side strategy relies on a tacit assumption about the quality of and access to those services as a path to improve human capital outcomes. Some conditional cash transfers have included supply-side complementary incentives to ensure that services are suitable to deliver a good education and better health. This study reviews the existing evidence on the impact of supply-side incentives in the context of conditional cash transfers. The review finds that a limited number of studies estimate effects of supply in human capital outcomes and only a few impact evaluations consider the role of schools or health centers in enabling access. The evaluations revised find no evidence that supply side interventions coupled with conditional cash transfers directly improve program outcomes. Nonetheless, several studies highlight the relevance of school organization, in terms of school modalities and student/teacher ratios in school enrollment and attendance. Impact estimations as well as the implementation of the supply-side programs also signal the need for a more nuanced understanding of how school management influences a variety of schooling outcomes. In general, the small number of impact estimations and the restricted set of variables used limits the generalizability of the results. For this reason, a principal conclusion of the review is the need for further research on the topic, as well as consistency across impact measures and a more in-depth analysis of school supply and their influence on learning outcomes.Global Policy Studie

Modeling peer assessment as a personalized predictor of teacher's grades: The case of OpenAnswer

Questions with open answers are rarely used as e-learning assessment tools because of the resulting high workload for the teacher/tutor that should grade them. This can be mitigated by having students grade each other's answers, but the uncertainty on the quality of the resulting grades could be high. In our OpenAnswer system we have modeled peer-assessment as a Bayesian network connecting a set of sub-networks (each representing a participating student) to the corresponding answers of her graded peers. The model has shown good ability to predict (without further info from the teacher) the exact teacher mark and a very good ability to predict it within 1 mark from the right one (ground truth). From the available datasets we noticed that different teachers sometimes disagree in their assessment of the same answer. For this reason in this paper we explore how the model can be tailored to the specific teacher to improve its prediction ability. To this aim, we parametrically define the CPTs (Conditional Probability Tables) describing the probabilistic dependence of a Bayesian variable from others in the modeled network, and we optimize the parameters generating the CPTs to obtain the smallest average difference between the predicted grades and the teacher's marks (ground truth). The optimization is carried out separately with respect to each teacher available in our datasets, or respect to the whole datasets. The paper discusses the results and shows that the prediction performance of our model, when optimized separately for each teacher, improves against the case in which our model is globally optimized respect to the whole dataset, which in turn improves against the predictions of the raw peer-assessment. The improved prediction would allow us to use OpenAnswer, without teacher intervention, as a class monitoring and diagnostic tool