Contrasting methods for classifying microtext statements containing mathametics

Published ArticleQueries received by tutors on the Dr Math mathematics tutoring service are created in a domain-specific form of microtext. The aim of the service is to help South African school learners to master mathematical concepts, but not all of the queries received on the service contain content relevant to the tutoring process. This paper contrasts various methods to classify learner queries automatically as relevant or not, in order to determine whether such a process could approximate human judgement. A back-propagation artificial neural network, a decision tree, a Bayesian filter, a k-means clustering algorithm and a rule-based filter are compared. The results of the classification techniques are contrasted with the results of three human coders, using the metrics of precision, recall, F-measure and the Pearson correlation co-efficient. Both the rule-based filter and neural network deliver classification results which closely reflect the classifications made by the human coders

A mathematics rendering model to support chat-based tutoring

Dr Math is a math tutoring service implemented on the chat application Mxit. The service allows school learners to use their mobile phones to discuss mathematicsrelated topics with human tutors. Using the broad user-base provided by Mxit, the Dr Math service has grown to consist of tens of thousands of registered school learners. The tutors on the service are all volunteers and the learners far outnumber the available tutors at any given time. School learners on the service use a shorthand language-form called microtext, to phrase their queries. Microtext is an informal form of language which consists of a variety of misspellings and symbolic representations, which emerge spontaneously as a result of the idiosyncrasies of a learner. The specific form of microtext found on the Dr Math service contains mathematical questions and example equations, pertaining to the tutoring process. Deciphering the queries, to discover their embedded mathematical content, slows down the tutoring process. This wastes time that could have been spent addressing more learner queries. The microtext language thus creates an unnecessary burden on the tutors. This study describes the development of an automated process for the translation of Dr Math microtext queries into mathematical equations. Using the design science research paradigm as a guide, three artefacts are developed. These artefacts take the form of a construct, a model and an instantiation. The construct represents the creation of new knowledge as it provides greater insight into the contents and structure of the language found on a mobile mathematics tutoring service. The construct serves as the basis for the creation of a model for the translation of microtext queries into mathematical equations, formatted for display in an electronic medium. No such technique currently exists and therefore, the model contributes new knowledge. To validate the model, an instantiation was created to serve as a proof-of-concept. The instantiation applies various concepts and techniques, such as those related to natural language processing, to the learner queries on the Dr Math service. These techniques are employed in order to translate an input microtext statement into a mathematical equation, structured by using mark-up language. The creation of the instantiation thus constitutes a knowledge contribution, as most of these techniques have never been applied to the problem of translating microtext into mathematical equations. For the automated process to have utility, it should perform on a level comparable to that of a human performing a similar translation task. To determine how closely related the results from the automated process are to those of a human, three human participants were asked to perform coding and translation tasks. The results of the human participants were compared to the results of the automated process, across a variety of metrics, including agreement, correlation, precision, recall and others. The results from the human participants served as the baseline values for comparison. The baseline results from the human participants were compared with those of the automated process. Krippendorff’s α was used to determine the level of agreement and Pearson’s correlation coefficient to determine the level of correlation between the results. The agreement between the human participants and the automated process was calculated at a level deemed satisfactory for exploratory research and the level of correlation was calculated as moderate. These values correspond with the calculations made as the human baseline. Furthermore, the automated process was able to meet or improve on all of the human baseline metrics. These results serve to validate that the automated process is able to perform the translation at a level comparable to that of a human. The automated process is available for integration into any requesting application, by means of a publicly accessible web service

Teacher Training for Response to Intervention at a Midwest University

The compelling force behind education is improving teacher quality. This research study was designed to find out how improving teacher quality in terms of RTI knowledge occurred at differing levels of instruction and how faculty perceived the changing role of general education teachers and administrators with regard to RTI. Participants in the study included 13 faculty instructors and 196 of their students. Instructors had experience ranging from three to 14 years and experience in public school settings varied from eight to 17 years teaching K-12 and from four to 20 years as administrators. The instrumentation and data collection consisted of the administration of a student survey and face-to-face interviews with education administrator students and faculty. This was a descriptive mixed methods study. For the quantitative phase, I used SPSS 21 to collate descriptive data and then to explore relationships among variables. The survey contained (a) Likert-style questions, with the resulting data classified as nominal or scalar; (b) questions that produced nominal data; and (c) questions that produced binary data. Descriptive data were provided for each variable based on its measurement level. Frequencies for each nominal variable were generated. Qualitative analysis showed that student teachers and administrators were learning about RTI, but apart from education majors, not through their coursework. Awareness of RTI correlated positively with the perceived effect of not being instructed in RTI; however, knowing more did not correlate with perceiving RTI to be important

A Delphi Study of the Potential Influence of Women in STEM Careers

American businesses are working with educational institutions to attract women into technical and scientific professions. However, less than one quarter of the people working in science, technology, engineering, and mathematics (STEM) are women. The educational system as-a-pipeline model is not supplying business with skilled workers, specifically female STEM employees. Organizational change must occur and this process begins with the organization\u27s leadership. Guided by the the conceptual frameworks of Kotter & Rathgeber and Kouzes & Posner, this Delphi study asked 54 female professionals, in various locations across the United States, about what influenced them in their education and career choices. Responses were collected from an internet survey and the emergent themes were deduced by graphical means using word clouds and word counts. The evaluation indicated that early interests in science were generated through networking experiences that occurred both in and out of the educational environment. Pro-male bias and lack of encouragement \u27influenced the women\u27s decision making while studying and working. To obtain the female professionals they need for the future, business leaders need to fund research, and provide internships, networking, and shadowing opportunities with current professionals. Leaders and managers also need to provide unbiased and supportive educational and workplace environments where women study and work. These social and organizational changes will allow women to become the needed workers for American businesses to maintain a technological presence in the world marketplace

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection

One goal of technical online communities is to help developers find the right answer in one place. A single question can be asked in different ways with different wordings, leading to the existence of duplicate posts on technical forums. The question of how to discover and link duplicate posts has garnered the attention of both developer communities and researchers. For example, Stack Overflow adopts a voting-based mechanism to mark and close duplicate posts. However, addressing these constantly emerging duplicate posts in a timely manner continues to pose challenges. Therefore, various approaches have been proposed to detect duplicate posts on technical forum posts automatically. The existing methods suffer from limitations either due to their reliance on handcrafted similarity metrics which can not sufficiently capture the semantics of posts, or their lack of supervision to improve the performance. Additionally, the efficiency of these methods is hindered by their dependence on pair-wise feature generation, which can be impractical for large amount of data. In this work, we attempt to employ and refine the GPT-3 embeddings for the duplicate detection task. We assume that the GPT-3 embeddings can accurately represent the semantics of the posts. In addition, by training a Siamese-based network based on the GPT-3 embeddings, we obtain a latent embedding that accurately captures the duplicate relation in technical forum posts. Our experiment on a benchmark dataset confirms the effectiveness of our approach and demonstrates superior performance compared to baseline methods. When applied to the dataset we constructed with a recent Stack Overflow dump, our approach attains a Top-1, Top-5, and Top-30 accuracy of 23.1%, 43.9%, and 68.9%, respectively. With a manual study, we confirm our approach's potential of finding unlabelled duplicates on technical forums.Comment: SANER 202

How to create a radically inclusive math classroom with regards to gender and sexual orientation

An extensive literature review was conducted to analyze how a secondary mathematics teacher can best support female and LGBTQ students. While extensive research has been conducted about the gender gap in STEM, concerning female students, little research has been done that includes LGBTQ students. While female students have made great improvements in mathematics participation and achievement, there is still work to be done. LGBTQ students face similar difficulties as female students but have not received the same attention and research as female students. This research indicates there are more things teachers should be doing to support students who have been historically marginalized in mathematics

Teaching Paleontology

This guide is designed to be used by teachers as an aid for teaching principles of fossils and past life to elementary school students. The activities and labs provided include topics such as fossilization, sedimentation, trace fossils, the importance of fossils, ancient environments, changes in environments, paleontology as a science, biodiversity, food webs, ecosystems, and human influences. The lessons provide pre- and post-questions, procedures, vocabulary, materials, and field trip ideas. This curriculum guide for paleontology was developed by Fossil Butte National Monument as part of its growing environmental education program. Educational levels: Intermediate elementary, Primary elementary

Latino/a First Generation Students in College: A Mixed Methods Review of Four-Decades of Literature

The research was a mixed methods review of the experiences of Latino/a First Generation students in college. Their experiences were identified through a quantitative component of identifying what had been published pertinent to this demographic and sectioning those publications into one of four decades when they were distributed. This quantitative portion of the research included a review of all published articles on the subject that appear in four scholarly, peer-reviewed journals from its inaugural issue to its final issue on December, 2020. The total publications analyzed were 5,103.The qualitative portion of the research comprised interviews of sixteen Latinos/as who were first in their families to attend college; four from each of four decades of research interest. Moreover, these participants were also identified as having attended an academic institution that was either a public, private, community college, or HBCU/HSI institution. Covid 19 mandates of social distancing were adhered to and interviews were conducted via virtual meeting software. Research findings indicated that financing college was of greatest concern to the research participants across all parameters, and this was corroborated with the number of publications on the topic; this held true for each decade of investigation. Additional areas of mixed-methodological agreement were related to Academic preparation; Teachers-mentors; Family Involvement, Structure & finances; Parental Expectations; Perseverance, Resiliency & Persistence; Access, Assistance & resources; Identity; and Community College. The findings led to two recommendations for institutional modification related to funding for education and pre-collegiate preparation programs like AVID and Puente. One additional recommendation was made to create a new perspective related to universities and their public school partners