Regular expression (regex) coding has advantages for text analysis. Humans are often able to quickly construct intelligible coding rules with high precision. That is, researchers can identify words and word patterns that correctly classify examples of a particular concept. And, it is often easy to identify false positives and improve the regex…

The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for…

Automatic speech recognition (ASR) has considerable potential to model aspects of classroom discourse with the goals of automated assessment, feedback, and instructional support. However, modeling student talk is besieged by numerous challenges including a lack of data for child speech, low signal to noise ratio, speech disfluencies, and…

Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually…

This paper examines the English grammatical errors and their patterns in the written assignments of a General Education course at City University of Hong Kong. Subjects are 60 local and non-local (exchange) undergraduate students who are all L2 learners with diversified education and disciplinary background (i. e. their major of study) which are…

Coding is a process of assigning meaning to a given piece of evidence. Evidence may be found in a variety of data types, including documents, research interviews, posts from social media, conversations from learning platforms, or any source of data that may provide insights for the questions under qualitative study. In this study, we focus on text…

Building on prior work visualizing player behavior using interaction networks [1], we examined whether measures of implicit science learning collected during gameplay were significantly related to changes in external pre-post assessments of the same constructs. As part of a national implementation study, we collected data from 329 high school…

A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a…

In online educational systems we can easily collect and analyze extensive data about student learning. Current practice, however, focuses only on some aspects of these data, particularly on correctness of students answers. When a student answers incorrectly, the submitted wrong answer can give us valuable information. We provide an overview of…

In this study we set out to investigate the errors made by students in logarithms. A test with 16 items was administered to 89 Secondary three students (Year 9). The errors made by the students were categorized using four categories from a framework by Movshovitz-Hadar, Zaslavsky, and Inbar (1987). It was found that students in the top third were…

The well-studied Baker et al., affect detectors on boredom, frustration, confusion and engagement concentration with ASSISTments dataset were used to predict state tests scores, college enrollment, and even whether a student majored in a STEM field. In this paper, we present three attempts to improve upon current affect detectors. The first…

There is much focus at the state and national levels in graduating students that are prepared for college and careers. In order for students to be prepared at the end of their K-12 education, indicators are also needed along the way about whether students are on track. Using historical state data, nine different methods were used to align…

This paper proposes a new typology for understanding common research errors that expands on the four types of error commonly discussed in the research literature. Examples are presented to illustrate Type I and Type II errors, errors related to the interpretation of statistically significant and nonsignificant results respectively, with attention…

In this paper we describe a number of types of errors and underlying misconceptions that arise in mathematical reasoning. Other types of mathematical reasoning errors, not associated with specific misconceptions, are also discussed. We hope the characterization and cataloging of common reasoning errors will be useful in studying the teaching of…

The purpose of this study is to contribute to an understanding of how errors could be employed in mathematics instruction so that the students use them constructively in support of their learning of mathematics. A teaching experiment was designed to create an ideal context in which the pedagogical approach to errors as springboards could be…