Scoping a vision for formative e-assessment: a project report for JISC

Assessment is an integral part of teaching and learning. If the relationship between teaching and learning were causal, i. e. if students always mastered the intended learning outcomes of a particular sequence of instruction, assessment would be superfluous. Experience and research suggest this is not the case: what is learnt can often be quite different from what is taught. Formative assessment is motivated by a concern with the elicitation of relevant information about student understanding and / or achievement, its interpretation and an exploration of how it can lead to actions that result in better learning. In the context of a policy drive towards technology-enhanced approaches to teaching and learning, the question of the role of digital technologies is key and it is the latter on which this project particularly focuses. The project and its deliverables have been informed by recent and relevant literature, in particular recent work by Black andIn this work, they put forward a framework which suggests that assessment for learning their term for formative assessment can be conceptualised as consisting of a number of aspects and five keystrategies. The key aspects revolve around the where the learner is going, where the learner is right now and how she can get there and examines the role played by the teacher, peers and the learner. Language: English Keywords: assessments, case studies, design patterns, e-assessmen

Discovering learning processes using inductive miner: A case study with learning management systems (LMSs)

Resumen tomado de la publicaciónDescubriendo procesos de aprendizaje aplicando Inductive Miner: un estudio de caso en Learning Management Systems (LMSs). Antecedentes: en la minería de procesos con datos educativos se utilizan diferentes algoritmos para descubrir modelos, sobremanera el Alpha Miner, el Heuristic Miner y el Evolutionary Tree Miner. En este trabajo proponemos la implementación de un nuevo algoritmo en datos educativos, el denominado Inductive Miner. Método: hemos utilizado datos de interacción de 101 estudiantes universitarios en una asignatura de grado desarrollada en la plataforma Moodle 2.0. Una vez prepocesados se ha realizado la minería de procesos sobre 21.629 eventos para descubrir los modelos que generan los diferentes algoritmos y comparar sus medidas de ajuste, precisión, simplicidad y generalización. Resultados: en las pruebas realizadas en nuestro conjunto de datos el algoritmo Inductive Miner es el que obtiene mejores resultados, especialmente para el valor de ajuste, criterio de mayor relevancia en lo que respecta al descubrimiento de modelos. Además, cuando ponderamos con pesos las diferentes métricas seguimos obteniendo la mejor medida general con el Inductive Miner. Conclusiones: la implementación de Inductive Miner en datos educativos es una nueva aplicación que, además de obtener mejores resultados que otros algoritmos con nuestro conjunto de datos, proporciona modelos válidos e interpretables en términos educativos.Universidad de Oviedo. Biblioteca de Psicología; Plaza Feijoo, s/n.; 33003 Oviedo; Tel. +34985104146; Fax +34985104126; [email protected]

Stability and sensitivity of Learning Analytics based prediction models

Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation

Virtual pedagogical model: development scenarios

info:eu-repo/semantics/publishedVersio

Exploring self-regulation through learning navigation pathways in online learning during the pandemic

Abstract. Online learning has shown significant growth as a powerful alternative method to deliver learning through the pandemic situation. In the meantime, many studies have been attempting to investigate how to provide education within online platforms effectively; however, a few have examined how students regulate their learning during online courses. Through the lens of self-regulated learning theory and Zimmerman’s cyclical model (2000), the present study examines how successful students and less successful students regulate their learning in hypermedia contexts. Moreover, the research aims to explore self-regulatory behaviors via the learning pathways between successful students and less successful students in a learning management system. The process-oriented method was applied to investigate the student’s learning paths from the log data collected. The coding was done based on a new coding scheme created through the lens of self-regulated learning theories, in which half of the events were assigned with self-regulatory activities due to the lack of theoretical explanation. The frequency analysis and process mining analysis of coded learning events were generated to examine the differences in self-regulated learning between successful and less successful students. The results indicate how successful and less successful students regulate differently in their learning navigation. For educators, the study provides insights to better design online learning courses and suggests self-regulatory strategies to support students in hypermedia contexts

Development of Self-Regulated Learning Skills Within Open-Ended Computer-Based Learning Environments for Science

Over the past decade, open-ended computer-based learning environments have been increasingly used to facilitate students’ learning of complex scientific topics. The non-linearity and open-endedness of these environments create learning opportunities for students, but can also impose challenges in terms of extraneous cognitive load and greater requirements for self-regulated learning (SRL). SRL is crucial for academic success in various educational settings. This dissertation explores how self-regulatory skills develop and the role of gender in the development of SRL skills in Virtual Performance Assessments (VPA), an immersive, open-ended virtual environment designed to assess middle school students’ science inquiry skills. Findings from three analyses combining educational data mining techniques with multilevel modeling indicated that students developed self-regulatory behaviors and strategies as they used VPA. For example, experience with VPA prepared students to adopt more efficient note-taking and note-reviewing strategies. Students who used VPA for the second time engaged in note-taking more frequently, noted a significantly higher quantity of unique information, used the control of variables strategy more frequently in note-taking, and reproduced more domain-specific declarative information in notes than students who used VPA for the first time, all of which have been found to be positively associated with science inquiry performance. Students also learned to exploit more available sources of information by applying learning strategies, in order to either solve inquiry problems, or to monitor and evaluate their solutions. Compared to the second-time users who focused primarily on answering the core inquiry question and selectively collected data, the first-time users’ behaviors showed the repetition and combination of exploratory actions such as talking with non-player characters and collecting data. In addition, consistent gender differences in SRL were observed in this study. Female students were more likely to take notes than male students; they took notes and reviewed notes more frequently and recorded a higher quantity of information in notes, especially information from the research kiosk. Females were also more likely to review notes or read research pages to assist them with the problem-solving and decision-making process than their male counterparts. Possibly due to the higher quantity of information recorded by female note-takers and their tendency to review notes over males, female students’ performance on science inquiry tasks improved across the course of using the two scenarios of VPA, whereas the male students’ science inquiry skills did not show improvement. Results from this dissertation study provide insights into the instructional design of personalized open-ended learning environments to facilitate self-regulated learning for both male and female students

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future