The adequate emotional state of students has proved to be essential for favoring learning. This paper explores the possibility of obtaining students' feedback about the emotions they feel in class in order to discover potential emotion patterns that might indicate learning fails. This paper presents a visual dashboard that allows students to track their emotions and follow up on their evolution during the course. We have compiled the principal classroom related emotions and developed a two-phase inquiry process to: verify the possibility to measure students' emotions in classroom; discover how emotions can be displayed to promote self-reflection; and confirm the impact of emotions on learning performance. Our results suggest that students' emotions in class are related to evaluation marks. This shows that early information about students' emotions can be useful for teachers and students to improve classroom results and learning outcomes.
Bayesian Knowledge Tracing (BKT) is one of the most popular knowledge inference models due to its interpretability and ability to infer student knowledge. A proper student modeling can help guide the behavior of a cognitive tutor system and provide insight to researchers on understanding how students learn. Using four different datasets we study the relationship between the error coming from adjusting the parameters and the difficulty index of the skills and the effect of the size of the dataset in this relationship.
Evidence of the month: February 2015
The field of learning analytics is working to define frameworks that can structure the legal and ethical issues that scholars and practitioners have to take into account when planning and applying analytics to their learning contexts. However, the main focus of this work to date has been higher education. This paper reflects on the need to extend these ethical frameworks to cover other approaches to learning analytics; specifically, small-scale classroom-oriented approaches designed to support teachers in their practice.
This reflection is based on three studies in which a teacher-led learning analytics approach was employed in higher education and primary school contexts. The paper describes the ethical issues that emerged in these learning scenarios, and discusses them with regard to: the overall learning analytics approach, the particular solution to learning analytics adopted, and the educational contexts where the analytics were applied.
This work is presented as a first step towards the wider objective of providing a more comprehensive and adapted ethical framework to learning analytics that is able to address the needs of different learning analytics approaches and educational contexts.
Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights.