Analítica de los usos digitales y rendimiento académico. Un estudio de caso con estudiantes universitarios

INTRODUCCIÓ. L’estudi que es presenta analitza l’empremta dels usos i registres digitals que els estudiants d’un curs presencial deixen en els espais virtuals complementaris, i analitza també les qualificacions que obtenen aquests estudiants en les fases d’avaluació continuada i final. L’objectiu és trobar si hi ha relació positiva entre el major ús i participació dels estudiants en les plataformes digitals i les qualificacions obtingudes. MÈTODE. S’adopta un disseny quantitatiu de tipus descriptiu i no experimental. La població analitzada es correspon amb el curs d’Anàlisi Microeconòmica Avançada, del grau d’Economia (GECO) de la Universitat Complutense de Madrid. RESULTATS. Els resultats mostren un ús digital tradicional i polaritzat dels estudiants, que defineixen tipologies i patrons diferenciats d’aprenentatge, molt condicionat a la programació de l’avaluació continuada i correlacionat positivament amb les qualificacions obtingudes. DISCUSSIÓ. La relació entre usos digitals i resultats de l’aprenentatge és un tema encara poc explorat en la literatura. Contribueix a la reflexió dels docents i pot reorientar el procés d’ensenyament, per evitar que els estudiants es vagin quedant endarrerits en l’ús digital i aconseguir que adaptin el ritme a l’avanç del curs i tinguin millors resultats acadèmics.INTRODUCTION: The study reports on the use made by a group of university students of digital technology during a face-to-face course by analysing the digital record they left in the virtual learning environments that were complementary to the course; it also reports on the grades and marks the students obtained in the formative and summative phases of the course assessment. The objective was to determine whether there is a positive relationship between the greater use digital platforms and higher grades and marks. METHOD: This quantitative study used non-experimental descriptive research to examine a population of economics undergraduates completing a course in advanced microeconomic analysis at the Universidad Complutense de Madrid. RESULTS: The students' use of digital technology was traditional and showed polarity, meaning that it was characterised by different typologies and patterns of learning. Usage was also very much conditioned by the programming of formative assessment and was positively correlated with grades and marks. DISCUSSION: There is a need for more research on the relationship between digital usage and learning outcomes. This research will contribute to teacher reflection and reorient the teaching process, ultimately helping students who are lagging behind in digital usage to keep abreast of their learning and achieve better academic results.INTRODUCCIÓN: El estudio que se presenta analiza los usos y registros digitales de los estudiantes en un curso presencial, que a modo de huella dejan en los espacios virtuales complementarios, así como las calificaciones que obtienen en las fases de evaluación continua y final. El objetivo es encontrar si existe relación positiva entre el mayor uso y participación de los estudiantes en las plataformas digitales y las calificaciones obtenidas. MÉTODO: Se adopta un diseño cuantitativo de tipo descriptivo y no experimental. La población analizada se corresponde con el curso de Análisis Microeconómico Avanzado, del grado en Economía (GECO), en la Universidad Complutense de Madrid. RESULTADOS: Los resultados muestran un uso digital tradicional y polarizado de los estudiantes, que definen tipologías y patrones diferenciados de aprendizaje, muy condicionado a la programación de la evaluación continua, y correlacionado positivamente con las calificaciones obtenidas. DISCUSIÓN: La relación entre usos digitales y resultados del aprendizaje es un tema aún poco explorado en la literatura, que contribuye a la reflexión del docente y puede reorientar el proceso de enseñanza, para evitar que los estudiantes que se van quedando rezagados en el uso digital acompasen el ritmo al avance del curso y tengan mejores resultados académicos

A Visual Analytics Method for Score Estimation in Learning Courses

The provision of awareness is a well-known method for fostering students' self-reflection, a metacognitive skill often related to academic success and considered one of the key skills of the 21st century. Although the information discovered using learning analytics techniques can be useful in fostering self-reflection, its delivery to students should be done without distracting them from their learning goals. This paper presents a visualization technique based on similarity measures and their relationship with final course results, in order to foster students' awareness. The approach is based on the idea that 'students that behave similarly are graded similarly'. This idea is validated with an empirical evaluation to determine the visualization technique's accuracy when used to find a relationship between similarity and grade. The study used a previously collected dataset and several volunteers were asked to estimate the students' scores with graphics provided as the only source of information. The obtained results validate the proposal as a means to foster effective self-reflection

A Visual Analytics Method for Score Estimation in Learning Courses

The provision of awareness is a well-known method for fostering students' self-reflection, a metacognitive skill often related to academic success and considered one of the key skills of the 21st century. Although the information discovered using learning analytics techniques can be useful in fostering self-reflection, its delivery to students should be done without distracting them from their learning goals. This paper presents a visualization technique based on similarity measures and their relationship with final course results, in order to foster students' awareness. The approach is based on the idea that 'students that behave similarly are graded similarly'. This idea is validated with an empirical evaluation to determine the visualization technique's accuracy when used to find a relationship between similarity and grade. The study used a previously collected dataset and several volunteers were asked to estimate the students' scores with graphics provided as the only source of information. The obtained results validate the proposal as a means to foster effective self-reflection

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data