Optimization of Blended Learning for Open Education From the Perspective of Instructional Interaction: A Case Study of English for the Humanities

One of the advantages of open education is the potential integration of online and offline learning. However, in practice, this integration has not been fully achieved. This study explores attempts to optimize blended learning for open education by promoting instructional interaction, which is essential for successful learning. Using English for Humanities as an example, the study found that interaction between learners and learning resources can be promoted by enriching materials and designing tasks with appropriate difficulty levels based on real-life situations. When an open discourse learning environment is established and daily communication is enhanced, students become more active in social interaction. The optimized blended learning approach has been shown to improve students’ learning participation and satisfaction. However, the study also revealed that the online interaction between students and resources remains at a relatively low level due to a lack of effective supervision and timely guidance. This practical study provides methods to promote instructional interaction and effective blended learning for open education.

A High Efficiency and Clean Combustion Strategy for Compression Ignition Engines: Integration of Low Heat Rejection Concepts with Low Temperature Combustion

Reciprocating engines are pervasively used in the transportation industry. The transportation industry is centered on achieving two important but often conflicting goals: 1) improved energy efficiency and 2) decreased pollution. Advanced engine technology seeks to accomplish these two goals, but there are technical barriers to implementation. For example, implementing an advanced combustion technology known as low temperature combustion (LTC) results in substantially decreased oxides of nitrogen and particulate matter emissions, but increased unburned hydrocarbons and carbon monoxide emissions that can also decrease engine efficiency. This study proposed a technology aiming to develop a solution to achieve improved energy conversion efficiency and lower emissions of internal combustion engines. The basic idea is to integrate low heat rejection (LHR) concepts with low temperature combustion engine. A comprehensive analysis of engine performance and fuel consumption was conducted to study low heat rejection concepts in the light-duty diesel engine under both conventional and low temperature combustion modes. From most previous studies on LHR diesel engines, thermal-barrier coatings (TBCs) have been recognized as a conventional way to insulate engine parts. The LHR concept proposed in this study, however, is realized by altering engine coolant temperature (ECT). In previous experiments, the studied engine was overcooled to low ECTs and then increased to 100˚C in an effort to get trend-wise behavior without exceeding safe ECTs. This study uses a 1-D engine simulation of the conventional multi-cylinder, four-stroke, 1.9-L diesel engine operating at 1500 rpm to examine the engine performance and emissions at different ECTs. From the comparative study between conventional-LHR and LTC-LHR modes, it is found that implementing LHR yields more significant improvements in fuel conversion efficiency with LTC mode than it does for the conventional mode, pointing to a higher sensitivity to variations in ECT. The potential reasons causing the difference in engine performance are addressed mainly by comparing the effects of ECT on the combustion phasing between two modes. The results indicate that the integration of LHR with LTC leads the phasing of combustion toward favorable changes, which partly contributes to the significantly improved efficiency

Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks

When constructing models that learn from noisy labels produced by multiple annotators, it is important to accurately estimate the reliability of annotators. Annotators may provide labels of inconsistent quality due to their varying expertise and reliability in a domain. Previous studies have mostly focused on estimating each annotator's overall reliability on the entire annotation task. However, in practice, the reliability of an annotator may depend on each specific instance. Only a limited number of studies have investigated modelling per-instance reliability and these only considered binary labels. In this paper, we propose an unsupervised model which can handle both binary and multi-class labels. It can automatically estimate the per-instance reliability of each annotator and the correct label for each instance. We specify our model as a probabilistic model which incorporates neural networks to model the dependency between latent variables and instances. For evaluation, the proposed method is applied to both synthetic and real data, including two labelling tasks: text classification and textual entailment. Experimental results demonstrate our novel method can not only accurately estimate the reliability of annotators across different instances, but also achieve superior performance in predicting the correct labels and detecting the least reliable annotators compared to state-of-the-art baselines.Comment: 9 pages, 1 figures, 10 tables, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL2019