Mining and Visualizing Usage of Educational Systems Using Linked Data

This work introduces a case study on usage of semantic context modelling and creation of Linked Data from logs in educational systems like a Personal Learning Environment (PLE) with focus on improvements and monitoring such systems, in generally, with respect to social, functional, user and activity centric level [7,15]. The case study demonstrates the application of semantic modelling of the activity context, from data collected over two years from the PLE at Graz University of Technology, using adequate domain ontologies, semantic technologies and visualization as reflection for potential technical and functional improvements. As it will be shown, this approach offers easy interfacing and extensibility on technological level and fast insight on statistical and preference trends for analytic tasks

ADHE: A Tool to Characterize Higher Education Dropout Phenomenon

The field of academic analytics emerged in higher education institutions (HEI) because of developments in database technologies and the generalization of data-mining practices and business intelligence tools. We have designed and implemented a dashboard called ADHE (Academic Analytical Dashboard in Higher Education) for a Colombian higher education institution. The purpose of ADHE is to help administrators of academic programs in their decision-making process regarding the analysis of the phenomenon of student dropout. We used the pipeline methodology for processing large volumes of data was used, which is based on five phases: data acquisition, integration, cleaning, transformation, and visualization. All phases were carried out in the R programming language using academic information sources from the Faculty of Engineering of the Universidad de Antioquia and the Colombian Institute for the Evaluation of Education. The dashboard ADHE is open for free and can be consulted at:  https://fhernanb.shinyapps.io/AppPermanencia/. The main findings were that social stratum, gender, and type of high school are associated with the student dropout phenomenon. Furthermore, in social stratum 1, male students and public high schools tend to have a higher student dropout proportion. Additionally, we conclude that admission to engineering programs requires a balance of qualitative and quantitative abilities. The dashboard ADHE should be used to help students, parents, teachers, and administrators understand student dropout dynamics

Modelo de análisis del rendimiento académico de la Unidad Educativa Personas Con Escolaridad Inconclusa. (P.C.E.I.) “Monseñor Leonidas Proaño” del cantón Latacunga, a través de minería de datos.

The main objective of this work is to contribute to the process of predicting the academic performance of the students of Unidad Educativa de Personas Con Escolaridad Inconclusa (PCEI) Monseñor Leonidas Proaño from Latacunga city by means of the integral study of techniques and tool of analysis of mining of data from the factors of influence such as the social, economic and academic, determining indicators that detect elements which will serve the teachers, authorities and educational mentors to improve the academic performance of the student in the process of education. One of the stages of this research was to design a theoretical model of student retention through the Statistical Package for Social Sciences (SPSS) software applying linear regression, ordinary least squares that allowed to create the theoretical model of academic performance. This model followed an experimental process with four classification algorithms through machine learning techniques such as J48, Random Forest, Naive Bayes and OneR, this process was used to predict the precision rate of the proposed model. The implementation of these techniques allowed us to determine that the Naive Bayes algorithm presents an accuracy rate of 88.85%, which indicates that the model presented is adequate in terms of reliability, the layer levels obtained through the experimental process with a result of 0.86 indicate that these models are adequate to predict students retention.El objetivo principal de este trabajo es contribuir al proceso de predicción del rendimiento académico de los estudiantes de la Unidad Educativa de Personas Con Escolaridad Inconclusa (PCEI) Monseñor Leonidas Proaño de la ciudad de Latacunga mediante el estudio integral de técnicas y herramienta de análisis de minería de datos a partir de los factores de influencia como el social, económico y académico, determinando indicadores que detecten elementos que servirán a los docentes, autoridades y mentores educativos para mejorar el rendimiento académico del estudiante en el el proceso educativo. Una de las etapas de esta investigación fue el diseño de un modelo teórico de la retención estudiantil a través del software Statistical Package for Social Sciences (SPSS) a través de regresión lineal, mínimos cuadrados ordinarios que permitieron crear el modelo teórico del rendimiento académico Posteriormente este modelo siguió un proceso experimental con cuatro algoritmos de clasificación a través de técnicas de machine learning como J48, Random Forest, Naive Bayes y OneR, proceso que se utilizó para predecir la tasa de precisión del modelo propuesto. La implementación de estas técnicas permitió determinar que el algoritmo Naive Bayes presenta una tasa de precisión del 88.85% lo que indica que el modelo que se presenta es adecuado en términos de confiabilidad, los niveles de capa obtenidos a través del proceso experimental con un resultado del 0,86 indican que estos modelos son adecuados para predecir la retención estudiantil

Construction of a model to diagnose and forecast the academic performance of the students of the cadastral engineering curricular project of the Francisco José de Caldas District University, using time series and machine learning.

Este documento presenta la investigación para la construcción de un modelo de diagnóstico y pronostico del rendimiento académico del proyecto curricular de Ingeniería Catastral utilizando las herramientas series de tiempo y Machine Learning. Se documenta un análisis de los resultados obtenidos en las investigaciones preliminares. La finalidad es conocer las características generales del alumno y las motivaciones para el abandono de la carrera de Ingeniería, de esta forma poder pronosticar qué procesos o actividades se pueden mejorar con el paso del tiempo para mejorar el rendimiento académico del estudiante. Consecuentemente, se evaluará y determinará el aporte de las series de tiempo y el aprendizaje automático para llegar a resultados estadísticos de datos con resultados fiables y efectivos para la toma de decisiones.This document presents the research for the construction of a diagnostic and prognostic model of the academic performance of the Cadastral Engineering curricular project using the tools of time series and Machine Learning. An analysis of the results obtained in the preliminary investigations is documented. The purpose is to know the general characteristics of the student and the motivations for abandoning the Engineering career, in this way to be able to predict which processes or activities can be improved over time to improve the student's academic performance. Consequently, the contribution of time series and machine learning will be evaluated and determined to arrive at statistical data results with reliable and effective results for decision making

Construcción de un modelo para determinar el rendimiento académico de los estudiantes basado en learning analytics (análisis del aprendizaje), mediante el uso de técnicas multivariantes

Falta palabras claveLas plataformas de enseñanza virtual tales como WEbCT, Moodle, Blackboard, Claroline, Dokeos y recientemente las plataformas MOOC (Massive Open Online Courses) permiten a las universidades monitorizar en tiempo real la actividad de los estudiantes. La integración de esta información con otras variables está en el origen de las técnicas de extracción de conocimiento útil para la mejora del proceso de enseñanza – aprendizaje, conocidas como análisis del aprendizaje (learning analytics). El objetivo central de la tesis es emplear el análisis del aprendizaje para identificar los factores y covariables que influyen en el rendimiento académico de los estudiantes universitarios, y construir un modelo multivariante de cómo influyen. Las preguntas que ha pretendido responder la investigación son: ¿Qué proporción de la variación en el rendimiento académico puede atribuirse a las variables que engloba el learning analytics?; ¿Cuál es la influencia que existe entre variables sociodemográficas y académicas sobre el rendimiento académico del colectivo de estudiantes universitarios ecuatorianos de modalidad a distancia?; ¿Existe una relación entre el rendimiento académico y el contexto de los estudiantes y aulas así como entre éstas dos a través del contexto de las escuelas?. Para ello en el capítulo 1 se revisan los tipos de análisis de datos que se están aplicando actualmente en el ámbito educativo, como son: Data Mining , Academic Analytics y el propio análisis del aprendizaje, ampliando la revisión de este último. En el capítulo 2 se hace una revisión teórica sobre el rendimiento académico y de los modelos estadísticos que se han venido aplicando a la hora de medirlo. El capítulo 3 recoge una revisión de las técnicas estadísticas aplicadas en la investigación educativa. En el capítulo 4 se introduce la metodología de estudio, selección de casos y variables que permiten justificar la elección de los modelos multivariantes. En el capítulo 5 se obtienen los resultados del modelo empírico multinivel estimando el modelo jerárquico con 2 y 3 niveles: estudiante (nivel inferior), aula (nivel intermedio) y escuela (nivel superior), utilizando el software Stata/SE 12.0. En el capítulo 6 se desarrolla un modelo logístico bivariante binario y un modelo logístico bivariante ordinal, los parámetros de los modelos se estiman usando el software R con la plataforma RStudio. En el capítulo 7 se presentan los resultados, así respecto al modelo multinivel, el de mejor ajuste para el rendimiento académico incluye: Tres covariables del Nivel 2: tasa de repetidores, ciclo y tipo de docente; Ocho variables del Nivel 1: edad, rinde supletorio, repite materia, participa en chat, participa en foro, participa en videocolaboración, N° comentarios, N° accesos al LMS; Cuatro interacciones multinivel; La varianza de cinco pendientes del Nivel 1. Los modelos logísticos bivariantes permiten confirmar que las covariables más relevantes son la edad de ingreso a la universidad y la participación activa en línea. Esta investigación, al identificar la influencia que ejercen sobre el rendimiento académico las variables consideradas, permite a las instituciones educativas mejorar la focalización de las intervenciones y los servicios de apoyo a estudiantes con mayor riesgo de fracaso académico

Mining reality to explore the 21st century student experience

Understanding student experience is a key aspect of higher education research. To date, the dominant methods for advancing this area have been the use of surveys and interviews, methods that typically rely on post-event recollections or perceptions, which can be incomplete and unreliable. Advances in mobile sensor technologies afford the opportunity to capture continuous, naturally-occurring student activity. In this thesis, I propose a new research approach for higher education that redefines student experience in terms of objective activity observation, rather than a construct of perception. I argue that novel, technologically driven research practices such as ‘Reality Mining’—continuous capture of digital data from wearable devices and the use of multi-modal datasets captured over prolonged periods, offer a deeper, more accurate representation of students’ lived experience. To explore the potential of these new methods, I implemented and evaluated three approaches to gathering student activity and behaviour data. I collected data from 21 undergraduate health science students at the University of Otago, over the period of a single semester (approximately four months). The data captured included GPS trace data from a smartphone app to explore student spaces and movements; photo data from a wearable auto-camera (that takes a photo from the wearer’s point-of-view, every 30 seconds) to investigate student activities; and computer usage data captured via the RescueTime software to gain insight into students’ digital practices. I explored the findings of these three datasets, visualising the student experience in different ways to demonstrate different perspectives on student activity, and utilised a number of new analytical approaches (such as Computer Vision algorithms for automatically categorising photostream data) to make sense of the voluminous data generated. To help future researchers wanting to utilise similar techniques, I also outlined the limitations and challenges encountered in using these new methods/devices for research. The findings of the three method explorations offer some insights into various aspects of the student experience, but serve mostly to highlight the idiographic nature of student life. The principal finding of this research is that these types of ‘student analytics’ are most readily useful to the students themselves, for highlighting their practices and informing self-improvement. I look at this aspect through the lens of a movement called the ‘Quantified Self’, which promotes the use of self-tracking technologies for personal development. To conclude my thesis, I discuss broadly how these methods could feature in higher education research, for researchers, for the institution, and, most importantly, for the students themselves. To this end, I develop a conceptual framework derived from Tschumi’s (1976) Space-Event-Movement framework. At the same time, I also take a critical perspective about the role of these types of personal analytics in the future of higher education, and question how involved the institution should be in the capture and utilisation of these data. Ultimately, there is value in exploring these data capture methods further, but always keeping the ‘student’ placed squarely at the centre of the ‘student experience’