Finding customer behavior insights for content creation in material and product sourcing using specialized topic analysis

In content creation, customer behavior insights are very important as they help creators find and create the content that drives sales. To comprehend customer needs, content creators need not just generalized information but also specific information, which can be different across markets and cultures. This information also needs some standards so it can be analyzed systematically. This paper aims to obtain customer insight into web content. Inside the web content, one possible source of this information is the tags based on customer feedback and the related entities. In this case, the product review data were collected and analyzed. However, manually analyzing feedback is a time-consuming activity. In this work, we formulated the topic analysis problem specialized for material and product sourcing, which could benefit product analysis and development. Technically, we also compared different text processing and classification methods, which set the benchmarks for reviewing the model performance in the future

Evaluation of Metacognitive and Self-Regulatory Programmes for Learning, Pedagogy and Policy in Tertiary EFL Contexts

In the past few decades, numerous attempts have been made to promote thinking skills and improve learning standards. Among these efforts, thinking skills have been widely advocated in higher education policy and practice. For this reason, the notion of metacognitive and self-regulatory development has been extensively applied in various educational contexts, including English language education. Metacognitive and self-regulatory development is perceived to play an essential role in regulating reflective thinking, learning and agency. Emerging evidence also suggests that metacognitive and self-regulatory development is associated with improved learning outcomes. To examine the existing evidence on the impact of metacognitive interventions in tertiary English as a Foreign Language (EFL) contexts, a systematic review was conducted. For this purpose, a priori review protocol was developed to minimise bias in identifying, screening and including studies for quality assessment and synthesis. The results from a meta-analysis indicate the potential of the metacognitive approach for EFL learning. However, the evidence remains inconclusive due to the limitations in the included studies. The findings from a thematic synthesis highlight the vital role of explicit instruction, clear pedagogical sequencing and the regulation of skills, rather than mere teaching about metacognition, for successful metacognitive development programmes. Informed by the findings of the systematic review and a pilot study, the main study in the thesis was designed to assess the effectiveness of a metacognitive intervention on students’ learning and metacognitive awareness. The study design is a cluster randomised controlled trial conducted with a target group of approximately 800 tertiary Thai EFL learners in the southernmost areas of Thailand. They were faced with a new education policy challenge requiring students to pass a standardised English test to satisfy the additional graduation criteria. The study included fourteen classes, twelve of which were randomly allocated as either an intervention or a control group. The other two non-randomised clusters were included for comparison. Standardised English tests and student questionnaires were used to collect data for the main outcomes. Classroom observations and interviews were conducted for process evaluation. Secondary data analysis and regression analysis were also included to gain more insights into the role of metacognition and self-regulation in EFL learning. The results suggest that the intervention group made more progress in the English language than their non-intervention peers. The intervention seems to offer more benefits to lower proficiency students than the more advanced ones. However, the impact of the intervention on metacognitive awareness had ambiguous results. The process evaluation describes both the positive features and the drawbacks of the intervention. The findings from multimethod analyses provide some valuable implications for learning, pedagogy and policy development

Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance

We introduce a new measure of distance between languages based on word embedding, called word embedding language divergence (WELD). WELD is defined as divergence between unified similarity distribution of words between languages. Using such a measure, we perform language comparison for fifty natural languages and twelve genetic languages. Our natural language dataset is a collection of sentence-aligned parallel corpora from bible translations for fifty languages spanning a variety of language families. Although we use parallel corpora, which guarantees having the same content in all languages, interestingly in many cases languages within the same family cluster together. In addition to natural languages, we perform language comparison for the coding regions in the genomes of 12 different organisms (4 plants, 6 animals, and two human subjects). Our result confirms a significant high-level difference in the genetic language model of humans/animals versus plants. The proposed method is a step toward defining a quantitative measure of similarity between languages, with applications in languages classification, genre identification, dialect identification, and evaluation of translations

Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction

Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659