A collaborative learning approach to dialogic peer feedback: a theoretical framework

Producción CientíficaFeedback has a powerful influence on learning. However, feedback practices in higher education often fail to produce the expected impact on learning. This is mainly because of its implementation as a one-way transmission of diagnostic information where students play a passive role as the information receivers. Dialogue around feedback can enhance students’ sense making from feedback and capacities to act on it. Yet, dialogic feedback has been mostly implemented as an instructor-led activity, which is hardly affordable in large classrooms. Dialogic peer feedback can offer a scalable solution; however, current practices lack a systematic design, resulting in low learning gains. Attending to this gap, this paper presents a theoretical framework that structures dialogic feedback as a three-phase collaborative activity, involving different levels of regulation: first, planning and coordination of feedback activities (involving socially shared regulation), second, feedback discussion to support its uptake (involving co-regulation), and last, translation of feedback into task engagement and progress (involving self-regulation). Based on the framework, design guidelines are provided to help practitioners shape their feedback practices. The application of the principles is illustrated through an example scenario. The framework holds great potential to promote student-centred approaches to feedback practices in higher education.Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)Junta de Castilla y León (Project VA257P18)European Commission (Project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Web Rule Languages to Carry Policies

Recent efforts in the area of Web policy languages show concerns on how to better represent both context and rules of a domain to deal with large number of resources and users. Interaction between domains with different business rules is also another questionable issue in this same area. Web rule languages have been recently introduced as a means to facilitate interaction between parties with dissimilar policies and business rules. Efforts have been placed to further review the possibility of the proposed solutions and extend them to work with other Web technologies. In this paper, we introduce REWERSE Rule Markup Language (R2ML) as a Web rule language that can be employed to make concepts, policies, and elements of a domain digestible by another domain through the use of vocabularies, rules, and annotations. We also show how R2ML elements can model the concepts and elements of different policy languages and assist systems with diverse policies with their interactions. 1

Towards Sharing Rules Between OWL/SWRL and UML/OCL

The paper presents a metamodel-driven model transformation approach to interchanging rules between the Semantic Web Rule Language along with the Web Ontology Language (OWL/SWRL) and Object Constraint Language (OCL) along with UML (UML/OCL). The solution is based on the REWERSE Rule Markup Language (R2ML), a MOF-defined general rule language, as a pivotal metamodel and the bidirectional transformations between OWL/SWRL and R2ML and between UML/OCL and R2ML. Besides describing mapping rules between three rule languages, the paper proposes the implementation by using ATLAS Transformation language (ATL) and describes the whole transformation process involving several MOF-based metamodels, XML schemas, EBNF grammars

Sharing OCL Constraints by Using Web Rules

This paper presents an MDE-based approach to interchanging rules between the Object Constraint Language (OCL) and REWERSE I1 Rule Markup Language (R2ML). The R2ML tends to be a standard rule markup language by following up the W3C initiative for Rule Interchange Format (RIF). The main benefit of this approach is that the transformations between languages are completely based on the languages' abstract syntax (i.e., metamodels) and in this way we keep the focus on the language concepts rather than on technical issues caused by different concrete syntax. In the current implementation, we have supported translation of the OCL invariants into the R2ML integrity rules. While most of the OCL expression could be represented in the R2ML and other rule languages, we have also identified that collection operators could only be partially supported in other rule languages (e.g., SWRL)

Ontologies to integrate learning design and learning content

Commentary on: Chapter 8: Basic Design Procedures for E-learning Courses (Sloep, Hummel & Manderveld, 2005)Abstract: The paper presents an ontology based approach to integrate learning designs and learning object content. The main goal is to increase the level of reusability of learning designs by enabling the use of a given learning design with different content. We first define a three-part conceptual model that introduces an intermediary level between learning design and learning objects called the learning object context. We then use ontologies to facilitate the representation of these concepts: LOCO is a new ontology for IMS-LD, ALOCoM is an existing ontology for learning objects, and LOCO-Cite is a new ontology for the contextual model. Building the LOCO ontology required correcting some inconsistencies in the present IMS LD Information Model. Finally, we illustrate the usefulness of the proposed approach on three use cases: finding a teaching method based on domain-related competencies, searching for learning designs based on domain-independent competencies, and creating user recommendations for both learning objects and learning designs.Editors: Colin Tattersall and Rob Koper

Towards Automatic Boundary Detection for Human-AI Hybrid Essay in Education

Human-AI collaborative writing has been greatly facilitated with the help of modern large language models (LLM), e.g., ChatGPT. While admitting the convenience brought by technology advancement, educators also have concerns that students might leverage LLM to partially complete their writing assignment and pass off the human-AI hybrid text as their original work. Driven by such concerns, in this study, we investigated the automatic detection of Human-AI hybrid text in education, where we formalized the hybrid text detection as a boundary detection problem, i.e., identifying the transition points between human-written content and AI-generated content. We constructed a hybrid essay dataset by partially removing sentences from the original student-written essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) Separated AI-generated content from human-written content during the embedding learning process; and (2) Calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two prototypes that have the furthest distance from each other. Through extensive experiments, we summarized the following main findings: (1) The proposed approach consistently outperformed the baseline methods across different experiment settings; (2) The embedding learning process (i.e., step 1) can significantly boost the performance of the proposed approach; (3) When detecting boundaries for single-boundary hybrid essays, the performance of the proposed approach could be enhanced by adopting a relatively large prototype size, leading to a $22$\% improvement (against the second-best baseline method) in the in-domain setting and an $18$\% improvement in the out-of-domain setting.Comment: 9 pages including references, 2 figure

Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level

Producción CientíficaDespite the recent surge of interest in learning analytics (LA), their adoption in everyday classroom practice is still slow. Knowledge gaps and lack of inter-stakeholder communication (particularly with educational practitioners) have been posited as critical factors for previous LA adoption failures. Yet, what issues should researchers, practitioners and other actors communicate about, when considering the adoption of an LA innovation in a particular context? We reviewed and synthesised existing literature on four focus areas related to LA, their adoption, implications for practice, and more general factors that have emerged as crucial when studying everyday classroom adoption of technologies (i.e., classroom orchestration). This synthesis resulted in two conversational frameworks and an inter-stakeholder communication tool. These can be used to guide and support conversations and decisionmaking about the adoption of LA innovations. We illustrate their usefulness with examples of use in ongoing LA adoption processes in Australia, Spain and Estonia.Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA082U16)Ministerio de Economía, Industria y Competitividad (Projects TIN2014-53199-C3-2-R and TIN2017-85179-C3-2-R),Ministerio de Educación, Cultura y Deporte (PRX17/00410