Topological data analysis (TDA) is a collection of powerful tools that can quantify shape and structure in data in order to answer questions from the data's domain. This is done by representing some aspect of the structure of the data in a simplified topological signature. In this article, we introduce two of the most commonly used topological…

Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound…

"The Times They Are a-Changin'" says the old Bob Dylan song. But it is not just the times that are a-changin'. For statistical literacy, the very earth is moving under our feet (apologies to Carole King). The seismic forces are (i) new forms of communication and discourse and (ii) new forms of data, data display and human interaction…

This chapter provides information on how the promise of analytics can be realized in gateway courses through a combination of good data science and the thoughtful application of outcomes to teaching and learning improvement efforts--especially with and among instructors.

An essential laboratory exercise for our lower-division electromagnetism course involves the measurement of Earth's local magnetic field from the emf induced in a rotating coil of wire. Although many methods exist for the measurement of Earth's field, this one gives our students some practical experience with Faraday's law. The apparatus we had…

Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional…

Collapsing categories is a commonly used data reduction technique; however, to date there do not exist principled methods to determine whether collapsing categories is appropriate in practice. With ordinal responses under the partial credit model, when collapsing categories, the true model for the collapsed data is no longer a partial credit…

Like other parts of the social system, education is becoming an information-driven venture: data technologies pervade all levels of the system. This datafication of education seems to take place alongside a general turn to learning that Gert Biesta has called learnification: a progressively singular focus on the manipulable features of individual…

An important aspect of qualitative research is reaching saturation--loosely, a point at which observing more data will not lead to discovery of more information related to the research questions. However, there has been no validated means of objectively establishing saturation. This article proposes a novel quantitative approach to measuring…

Physical literacy is developing as a contested concept with definitional blurring across international contexts, confusing both practitioners and researchers. This paper serves the dual purpose of reporting on an interrogation of concepts associated with physical literacy in academic writing and exploring the use of a text mining data analysis…

This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text-based self-evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information…

Phenomenology as a philosophy and a method of inquiry is not limited to an approach to knowing, it is rather an intellectual engagement in interpretations and meaning making that is used to understand the lived world of human beings at a conscious level. Historically, Husserl' (1913/1962) perspective of phenomenology is a science of understanding…

Cognitive diagnosis models (CDMs) allow for the extraction of fine-grained, multidimensional diagnostic information from appropriately designed tests. In recent years, interest in such models has grown as formative assessment grows in popularity. Many dichotomous as well as several polytomous CDMs have been proposed in the last two decades, but…

Big data analytics are prevalent in fields like business, engineering, public health, and the physical sciences, but social scientists are slower than their peers in other fields in adopting this new methodology. One major reason for this is that traditional statistical procedures are typically not suitable for the analysis of large and complex…

We introduce a model which combines principles from psychometric and connectionist paradigms to allow direct Q-matrix refinement via backpropagation. We call this model dAFM, based on augmentation of the original Additive Factors Model (AFM), whose calculations and constraints we show can be exactly replicated within the framework of neural…