ArticlePDF Available

Know thy students: Providing aggregate student data to instructors

Authors:

Abstract and Figures

Learner-centered approaches to higher education require that instructors have insight into their students' characteristics, but instructors often prepare their courses long before they have an opportunity to meet the students. To address this dilemma, we developed the institutional Student Profile Report, which informs instructors about the demographics, enrollment history, and general academic performance of the students in their upcoming classes before the start of the semester. Survey results indicated that instructors found the report interesting and useful, and our analysis of grade outcomes found that the report did not introduce bias in letter grade assignment.
Content may be subject to copyright.
A preview of the PDF is not available
... In [BJL15] the authors state that learner-centered approaches to higher education require that instructors have insight into their student?s characteristics before they start the course. ...
... The potential goals of learning analytics are diverse, including monitoring, prediction, tutoring, personalization, recommendation, etc. [MAUH12]; [KM15]. They can be categorized according to the targeted stakeholders: In [BJL15] the authors state that learner-centered approaches to higher education require that instructors have insight into their student's characteristics before they start the course. For that, they also agree that all the information about students should be given to them: demographics, enrollment history and general academic performance. ...
... 1. the influence and attitudes of the most important people in student's life, family and friends [Ren94], 2. community's involvement and attitudes [BJL15]. ...
Full-text available
Book
This document that aims to reflect the necessary aspects implied into the characterisation of a student profile: pedagogical characteristics, teaching learning attitudes, description of the different situations that may reflect problems regarding a normal progress. Also the characterisation the scenario that concerns the application of educational data mining techniques. The data that a student generates while progressing on his/her studies will be synthesised and related to potential profile features. As per the SPEET project concern, the definition of an IT architecture that is aimed at dealing with such student profile characterisation is also outlined.
... Blanket generalizations that treat an institution's "students" as a single group are likely to be either ineffectively vague, or not applicable to all members of the student population [20]. In the classroom, post-secondary instructors find value in knowing the differentiating characteristics of the students in their classes, and tailoring instruction to accommodate their unique attributes [17]. Data-driven interventions and analytical characterizations of student behaviors should also be sensitive to the differences between students. ...
Full-text available
Conference Paper
Analyses of student data in post-secondary education should be sensitive to the fact that there are many different topics of study. These different areas will interest different kinds of students, and entail different experiences and learning activities. However, it can be challenging to identify the distinct academic themes that students might pursue in higher education, where students commonly have the freedom to sample from thousands of courses in dozens of degree programs. In this paper, we describe the use of topic modeling to identify distinct themes of study and classify students according their observed course enrollments, and present possible applications of this technique for the broader field of educational data mining.
... Another product of learning analytics (besides actionable knowledge of learning processes) may be institution-wide datadriven dashboards and visualizations to inform teachers, advisors, and students themselves about learning behaviours and student properties (Govaerts, Verbert, Duval, & Pardo, 2012;Motz, Teague, & Shepard, 2015;Duval, 2011;Tervakari, Silius, Koro, Paukkeri, & Pirttila, 2014). In this mode, learning analytics is responsible for providing an analytical viewport to improve teaching and learning, enabling a user to become better-aware of student propensities (Verbert, Duval, Klerkx, Govaerts, & Santos, 2013). ...
Full-text available
Article
To identify the ways teachers and educational systems can improve learning, researchers need to make causal inferences. Analyses of existing datasets play an important role in detecting causal patterns, but conducting experiments also plays an indispensable role in this research. In this article, we advocate for experiments to be embedded in real educational contexts, allowing researchers to test whether interventions such as a learning activity, new technology, or advising strategy elicit reliable improvements in authentic student behaviours and educational outcomes. Embedded experiments, wherein theoretically relevant variables are systematically manipulated in real learning contexts, carry strong benefits for making causal inferences, particularly when allied with the data-rich resources of contemporary e-learning environments. Toward this goal, we offer a field guide to embedded experimentation, reviewing experimental design choices, addressing ethical concerns, discussing the importance of involving teachers, and reviewing how interventions can be deployed in a variety of contexts, at a range of scales. Causal inference is a critical component of a field that aims to improve student learning; including experimentation alongside analyses of existing data in learning analytics is the most compelling way to test causal claims.
... In Arts, information of this kind is routinely sought out by instructors, curriculum committees and heads to assist with planning for instruction or broader curriculum review. Motz et al. (2015) describe their pilot of such an undertaking at U. Indiana. See also the Academic Reporting Toolkit ART 2.0 ( http://digitaleducation.umich.edu/dei/academicreportingtools/ ) at the University of Michigan. ...
Full-text available
Chapter
During the past decades, the potential of analytics and data mining - methodologies that extract useful and actionable information from large datasets - has transformed one field of scientific inquiry after another (cf. Collins, Morgan, & Patrinos, 2004; Summers et al., 1992). Analytics has become a trend over the past several years, reflected in large numbers of graduate programs promising to make someone a master of analytics, proclamations that analytics skills offer lucrative employment opportunities (Manyika et al., 2011), and airport waiting lounges filled with advertisements from different consultancies promising to significantly increase profits through analytics. When applied to education, these methodologies are referred to as learning analytics (LA) and educational data mining (EDM). In this chapter, we will focus on the shared similarities as we review both parallel areas while also noting important differences. Using the methodologies we describe in this chapter, one can scan through large datasets to discover patterns that occur in only small numbers of students or only sporadically (cf. Baker, Corbett, & Koedinger, 2004; Sabourin, Rowe, Mott, & Lester, 2011); one can investigate how different students choose to use different learning resources and obtain different outcomes (cf. Beck, Chang, Mostow, & Corbett, 2008); one can conduct fine-grained analysis of phenomena that occur over long periods of time (such as the move toward disengagement over the years of schooling - cf. Bowers, 2010); and one can analyze how the design of learning environments may impact variables of interest through the study of large numbers of exemplars (cf. Baker et al., 2009). In the sections that follow, we argue that learning analytics has the potential to substantially increase the sophistication of how the field of learning sciences understands learning, contributing both to theory and practice.
Full-text available
Article
Nell’era di Internet, delle tecnologie mobili e dell’istruzione aperta, la necessità di interventi per migliorare l’efficienza e la qualità dell’istruzione superiore è diventata pressante. I big data e il Learning Analytics possono contribuire a condurre questi interventi, e a ridisegnare il futuro dell’istruzione superiore. Basare le decisioni su dati e sulle evidenze empiriche sembra incredibilmente ovvio. Tuttavia, l’istruzione superiore, un campo che raccoglie una quantità enorme di dati sui propri “clienti”, è stata tradizionalmente inefficiente nell’utilizzo dei dati, spesso operando con notevole ritardo nell’analizzarli, pur essendo questi immediatamente disponibili. In questo articolo, viene evidenziato il valore delle tecniche di analisi dei dati per l’istruzione superiore, e presentato un modello di sviluppo per i dati legati all’apprendimento. Ovviamente, l’apprendimento è un fenomeno complesso, e la sua descrizione attraverso strumenti di analisi non è semplice; pertanto, l’articolo presenta anche le principali problematiche etiche e pedagogiche connesse all’utilizzo delle tecniche di analisi dei dati in ambito educativo. Cionondimeno, il Learning Analytics può penetrare la nebbia di incertezza che avvolge il futuro dell’istruzione superiore, e rendere più evidente come allocare le risorse, come sviluppare vantaggi competitivi e, soprattutto, come migliorare la qualità e il valore dell’esperienza di apprendimento.
Full-text available
Article
Expert and novice mathematics and science teachers, along with a group of postulant teachers (content matter experts from business with a desire to teach but with no pedagogical training) participated in a simulated teaching task. All subjects were given extensive information about a class they were asked to take over and then questioned about their plans for instruction, and their recall of information about students. Analysis of the protocols resulting from these queries yielded nine propositions about how expert, novice, and postulant teachers process and use information differently. The differences and similarities among the three groups of subjects in ability to perceive, remember, and solve problems related to teaching indicate how expert teachers resemble experts in other fields and provide insight into the unique aspects of expertise in pedagogy.
Article
In this paper, I argue that the first day of class is an encounter among strangers who develop a definition of the situation as they perceive each other. For these reasons, special preparation is required. I suggest that one way to prepare is to make certain the following questions are addressed: What are the members of the class setting out to do together? How will they accomplish what they attempt? What will the instructor be like? Who are the students in the class? And what is the sociological substance of the course? Several strategies and techniques for handling each question are presented along with problems which are likely to remain regardless of which technique is used.
Article
Recently, learning analytics (LA) has drawn the attention of academics, researchers, and administrators. This interest is motivated by the need to better understand teaching, learning, “intelligent content,” and personalization and adaptation. While still in the early stages of research and implementation, several organizations (Society for Learning Analytics Research and the International Educational Data Mining Society) have formed to foster a research community around the role of data analytics in education. This article considers the research fields that have contributed technologies and methodologies to the development of learning analytics, analytics models, the importance of increasing analytics capabilities in organizations, and models for deploying analytics in educational settings. The challenges facing LA as a field are also reviewed, particularly regarding the need to increase the scope of data capture so that the complexity of the learning process can be more accurately reflected in analysis. Privacy and data ownership will become increasingly important for all participants in analytics projects. The current legal system is immature in relation to privacy and ethics concerns in analytics. The article concludes by arguing that LA has sufficiently developed, through conferences, journals, summer institutes, and research labs, to be considered an emerging research field.
Book
How do we make sense of other people and of ourselves? What do we know about the people we encounter in our daily lives and about the situations in which we encounter them, and how do we use this knowledge in our attempt to understand, predict, or recall their behavior? Are our social judgments fully determined by our social knowledge, or are they also influenced by our feelings and desires? Social cognition researchers look at how we make sense of other people and of ourselves. In this book Ziva Kunda provides a comprehensive and accessible survey of research and theory about social cognition at a level appropriate for undergraduate and graduate students, as well as researchers in the field. The first part of the book reviews basic processes in social cognition, including the representation of social concepts, rules of inference, memory, "hot" cognition driven by motivation or affect, and automatic processing. The second part reviews three basic topics in social cognition: group stereotypes, knowledge of other individuals, and the self. A final chapter revisits many of these issues from a cross-cultural perspective.
Article
Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international research project investigating which student online activities accurately predict academic achievement. Analysis of LMS tracking data from a Blackboard Vista-supported course identified 15 variables demonstrating a significant simple correlation with student final grade. Regression modelling generated a best-fit predictive model for this course which incorporates key variables such as total number of discussion messages posted, total number of mail messages sent, and total number of assessments completed and which explains more than 30% of the variation in student final grade. Logistic modelling demonstrated the predictive power of this model, which correctly identified 81% of students who achieved a failing grade. Moreover, network analysis of course discussion forums afforded insight into the development of the student learning community by identifying disconnected students, patterns of student-to-student communication, and instructor positioning within the network. This study affirms that pedagogically meaningful information can be extracted from LMS-generated student tracking data, and discusses how these findings are informing the development of a customizable dashboard-like reporting tool for educators that will extract and visualize real-time data on student engagement and likelihood of success.