Combining automated and peer feedback for effective learning design in writing practices

© 2017 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. The provision of formative feedback has been shown to support self-regulated learning for improving students' writing. Formative peer feedback is a promising approach, but requires scaffolding to be effective for all students. Automated tools making use of writing analytics techniques are another useful means to provide formative feedback on students' writing. However, they should be applied through effective learning designs in pedagogic contexts for better uptake and sense-making by students. Such learning analytics applications open up the possibilities to combine different types of feedback for effective design of interventions in authentic contexts. A framework combining peer feedback and automated feedback is proposed to design effective interventions for improving student writing. Automated feedback is augmented by peer feedback for better contextual feedback and sense making, and peer feedback is enhanced by automated feedback as scaffolding, thus complementing each other

Implementing learning analytics for learning impact: Taking tools to task

© 2020 Elsevier Inc. Learning analytics has the potential to impact student learning, at scale. Embedded in that claim are a set of assumptions and tensions around the nature of scale, impact on student learning, and the scope of infrastructure encompassed by ‘learning analytics’ as a socio-technical field. Drawing on our design experience of developing learning analytics and inducting others into its use, we present a model that we have used to address five key challenges we have encountered. In developing this model, we recommend: A focus on impact on learning through augmentation of existing practice; the centrality of tasks in implementing learning analytics for impact on learning; the commensurate centrality of learning in evaluating learning analytics; inclusion of co-design approaches in implementing learning analytics across sites; and an attention to both social and technical infrastructure

Augmenting pedagogic writing practice with contextualizable learning analytics

University of Technology Sydney. Connected Intelligence Centre.Academic writing is a key skill that contributes to essential learning outcomes for higher education students. Despite its importance, students often lack proficiency in writing and find it challenging to learn. While previous research suggests that students’ writing skills are enhanced through formative feedback, the time-consuming nature of providing formative feedback on individual student drafts, especially in large cohorts, makes it impractical for educators to provide detailed writing support in this way. A promising approach, therefore, is the use of to provide automated formative feedback on writing. This particular form of , using computational techniques and natural language processing, provides timely, immediate, and consistent automated feedback to help students improve their writing. However, for such tools to work effectively in pedagogic settings, and be adopted by practitioners, academics need to feel a sense of ownership over how the tool fits into their practice. This recognition motivates an increased emphasis on aligning learning analytics applications with learning design, so that analytics-driven feedback is congruent with the pedagogy and assessment regime. The thesis investigates how writing practice can be augmented with a writing analytics tool called ‘AcaWriter’ by aligning it with learning design. The approach is evaluated across two disciplines in authentic higher educational settings using a design-based research approach. Mixed methods and multiple data sources are used to examine how students perceive and interact with automated feedback, and revise their writing. Based on this analysis, the thesis provides empirical evidence that students found the writing intervention and automated feedback from AcaWriter useful, and improved their subject-related writing skills, thus validating its applicability in writing contexts. It identifies varied levels of student engagement with automated feedback and ways to scaffold its application for effective use. Cross-fertilizing research and practice, the key insights gained from these design iterations are formalised as the Design model. The model clarifies how the features, feedback and learning activities around AcaWriter can be tuned for different pedagogical contexts and assessment regimes, by co-designing them with educators. The thesis also studies the perspectives of educators, who play a key role in implementing such learning analytics innovations in their classrooms. The thesis advances theory and practice in the development of flexible learning analytics applications, capable of providing meaningful, contextualized support that enhances learning, and adoption by practitioners in authentic practice

Assessing the language of chat for teamwork dialogue

In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study will evaluate and explain preprocessing and classification methods used to analyze teamwork dialogue from a dataset of chat data. Analytics methods evaluated in this study provide a direction for assessing the language of chat for teamwork dialogue and can help extend the work of technology enhanced language learning to n ot only focus on academic competency, but on the communication aspect too

Advances in Writing Analytics: Mapping the state of the field

Writing analytics as a field is growing in terms of the tools and technologies developed to support student writing, methods to collect and analyze writing data, and the embedding of tools in pedagogical contexts to make them relevant for learning. This workshop will facilitate discussion on recent writing analytics research by researchers, writing tool developers, theorists and practitioners to map the current state of the field, identify issues and develop future directions for advances in writing analytics

The angioarchitecture of brain arteriovenous malformations and its' associa tion with intracranial haemorrhage: An analysis

Title: The angioarchitecture of brain arteriovenous malfonnations and its' association with intracranial haemorrhage: an analysis Introduction and objectives: Central nervous system arteriovenous malfonnation (AVM) is a vascular malfonnation of the brain and consists of a tangle of veins and arteries without an intervening capillary bed. It predominantly affects young male patients and presents with different clinical manifestations namely headache, seizures, neurological deficit and intracranial haemorrhage. The patients who present acutely with intracranial bleed have a significant morbidity and mortality. Thus, the aim is to study the angioarchitecture ofBA VM and determine intracranial bleed. This study also enabled us to look for the association between the volume of haematoma and the architecture of the brain arteriovenous malfonnation. The correlation between the features and risk of intracranial bleed is invaluable in predicting the behaviour of BA VM. Methodology: This was a cross sectional study where patients who attended the Department of Radiology were retrospectively collected from the'-year 2000. A total of 58 patients were included after excluding dural arteriovenous fistula and brain haemangiomas. The nidal size of the lesion and its maximum diameter were measured on cerebral angiogram. Venous drainage, feeding arteries aneurysms and location were further evaluated on cerebral angiogram and CT scan/MRI. The association between the angioarchitecture of BA VM and intracranial haemorrhage were analysed using multivariate analysis. The other objective to evaluate the association between angioarchitecture and volume of haematoma was detennined using univariate model. Results: In HUSM, BAVM was seen predominantly in a young male patient with a mean age of 26.67 (SD ±12.96). Small nidal size (p-value = 0.000), deep location (pvalue = 0.000) was found to be predictors of intracranial bleed. And deep venous drainage was significant at a univariate level only due to a small sample size. All patients with brain arteriovenous malfonnation and coexisting intracranial aneurysms presented with intracranial bleed. The angioarchitecture of BA VM detennining the volume of haematoma was not found to be significant statistically, but on clinical interpretation diffuse bleed was seen in 69 % deeply located, 63 % small sized and 66.7 % deep draining vein, 70 % deep arterial feeders and presence of coexisting aneurysms