This paper provides an alternative way of document representation by treating topic probabilities as a vector representation for words and representing a document as a combination of the word vectors. A comparison on summary data shows that this representation is more effective in document classification. [This paper was published in:…

Through colonialism, external forces can alter and shift social structures and practices. It causes trans-generational, often normalized, invisible, and profound marginalization of the collective identities of local and indigenous populations. Decolonization is the resisting and undoing of colonial impacts. It's the process of reforming a…

The growing use of machine learning for the data-driven study of social issues and the implementation of data-driven decision processes has required researchers to re-examine the often implicit assumption that datadriven models are neutral and free of biases. The careful examination of machine-learned models has identified examples of how existing…

Background/Context: As a major area of civic decision making, public education is a central arena for advocacy groups seeking to influence policy debates. An emerging body of research examines advocates' use of social media. While debates about policy can be thought of as a clash of large ideas contained within frames, cognitive linguists note…

This chapter presents an innovative, cross-disciplinary methodological approach to systematically reviewing and comparing large bodies of literature using big data, Natural Language Processing, network analysis, and supplementary qualitative analysis. The approach is demonstrated through an analysis of the literature surrounding four common…

The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively…

Providing both effective feedback applications and reliable assessment practices are two central issues in ESL/EFL writing instruction contexts. Giving individual feedback is very difficult in crowded classes as it requires a great amount of time and effort for instructors. Moreover, instructors likely employ inconsistent assessment procedures,…

Human-machine communication has emerged as a new relational context of education and should become a priority for instructional scholarship in the coming years. With artificial intelligence and robots offering personalized instruction, teachers' roles may shift toward overseers who design and select machine-led instruction, monitor student…

Education researchers have traditionally faced severe data limitations in studying local policy variation; administrative data sets capture only a fraction of districts' policy decisions, and it can be expensive to collect more nuanced implementation data from teachers and leaders. Natural language processing and web-scraping techniques can help…

Question classification is a key point in many applications, such as Question Answering (QA, e.g., Yahoo! Answers), Information Retrieval (IR, e.g., Google search engine), and E-learning systems (e.g., Bloom's tax. classifiers). This paper aims to carry out a systematic review of the literature on automatic question classifiers and the technology…

Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to…

Education researchers have traditionally faced severe data limitations in studying local policy variation; administrative datasets capture only a fraction of districts' policy decisions, and it can be expensive to collect more nuanced implementation data from teachers and leaders. Natural language processing and web-scraping techniques can help…

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…

The main purpose of the study is to develop a prototype of Google Dialog Flow (Dialog flow) for interacting with in-service school teachers on the content relating to the classroom research. The project has been carried out since the early of May 2020 and is still ongoing. This paper presenting the progress report as of October 2020 focuses on the…