A standard‐based architecture to support learning interoperability: A practical experience in gamification

Pérez-Berenguer, Daniel; Garcí­a-Molina, Jesús

doi:10.1002/spe.2572

Cited by 15 publications

(24 citation statements)

References 18 publications

(27 reference statements)

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“…In the following two sections, the CustomLA DSL and the incremental processing are explained in detail. UPCTforma has been supporting LA since early stages [1]. Figure 1 shows the UPCTforma components related to LA, as well as the two basic components of interoperability and deployment.…”

Section: Learning Analytics In Indieopenmentioning

confidence: 99%

“…Treating all captured events in each request of LA visualization is far from efficient as the number of registered events may grow considerably as the course progresses. This strategy was used in a first version of UPCTforma [1], and some teachers reported waiting times above two minutes in the dashboard visualization. The design of a more efficient approach to compute the summary data was therefore tackled.…”

Section: Incremental Processing Of Caliper Eventsmentioning

confidence: 99%

“…The Student Relationship Engagement System (SRES) is a LA tool devised to provide teachers several ways of ''customizing analysis to the needs of their students and courses'' [9], [11]. When using SRES , 1 teachers can decide which learning and teaching data to be collected, curate data with spreadsheets, indicate conditions to identify particular student groups, and select data to be analyzed. The data selected are imported from SIS and LMS systems and are analyzed using machine learning algorithms As for the conditions characterizing student groups, they are very simple and expressed through a query form which allows to select a column id, a relational operator, a value and its data type (e.g.…”

Section: A La Customization Approachesmentioning

confidence: 99%

“…The work of this center has been mainly aimed at developing an online content creation platform named INDIeOpen, which, as of today, consists of an infrastructure, named UPCTforma, that offers basic services, and an authoring tool, named INDIeAuthor, built on such an infrastructure. UPCTforma is based on the interoperability-based architecture presented in [1], and INDIeAuthor provides a family…”

Section: Introductionmentioning

confidence: 99%

“…A learning analytics software architecture is normally designed to implement an iterative 4-stages workflow [6], [7]: (i) capturing interaction events, (ii) collecting data to be analyzed from raw events, (iii) performing data analysis to obtain indicators, and (iv) visualizing indicators on dashboards that help teachers to gather insights on the learning process and adjust it for its improvement. In [1], an initial LA architecture for UPCTforma was described, and a gamification case study illustrated its application. Here, we will present how this architecture has evolved to satisfy two new requirements: to provide teachers an instrument to customize dashboards, and to improve the efficiency in the visualization of those dashboards when a very large number of events must be analyzed.…”

Section: Introductionmentioning

confidence: 99%

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A Customizable and Incremental Processing Approach for Learning Analytics

2020

Self Cite

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The ability of learning analytics to improve the learning/teaching processes is widely recognized. In this paper, the learning analytics architecture developed at the Digital Content Production Center of the Technical University of Cartagena (Spain) is presented. This architecture contributes to the field of learning analytics in two aspects: it allows for dashboard customization and improves the efficiency of the analysis of learners' interaction data. Events resulting from learners' interaction are captured and stored in Caliper standard format, to be further processed incrementally to allow dashboards to be shown without delay to teachers. Customization is considered a mandatory requirement for learning analytics tools, however, although some proposals have recently been made, a greater research effort in this topic is necessary. In the present work, this requirement is addressed by defining a domain-specific language (DSL) that allows teachers to customize dashboards. This language allows to express indicators (logical expressions) that classify students into different groups depending on their performance level. The paper also shows how our learning analytics approach was evaluated with a course that applies a flipped classroom method, and how it compares to the most relevant related works that have been published.

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Section: Learning Analytics In Indieopenmentioning

confidence: 99%

Section: Incremental Processing Of Caliper Eventsmentioning

confidence: 99%

Section: A La Customization Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Customizable and Incremental Processing Approach for Learning Analytics

2020

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A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

Gomez,

Ruipérez‐Valiente,

García Clemente

2023

Softw Pract Exp

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During the last few years, there has been increasing attention paid to serious games (SGs), which are games used for non‐entertainment purposes. SGs offer the potential for more valid and reliable assessments compared to traditional methods such as paper‐and‐pencil tests. However, the incorporation of assessment features into SGs is still in its early stages, requiring specific design efforts for each game and adding significant time to the design of Game‐based Assessments (GBAs). In this research, we present a completely novel framework that aims to perform interoperable GBAs by: (a) integrating a common GBA ontology model to process RDF data; (b) developing in‐game metrics to infer useful information and assess learners; (c) integrating a service API to provide an easy way to interact with the framework. We then validate our approach through performance evaluation and two use cases, demonstrating its effectiveness in real‐world scenarios with large‐scale datasets. Our results show that the developed framework achieves excellent performance, replicating metrics from previous literature. We anticipate that our work will help alleviate current limitations in the field and facilitate the deployment of GBAs as a Service.

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