Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground

Feedback on Academic Essay Writing through pre-Emptive Hints: Moving Towards "Advice for Action"

This paper adopts an “advice for action” approach to feedback in educational practice: addressing how provision of “hints” to participants before they write academic essays can support their understanding and performance in essay-writing tasks. We explored differences in performance by type of hint, and whether there was a transfer of better performance in subsequent essays. Fifty participants were recruited, consisting of eight men and 42 women aged 18-80. Participants were assigned in rotation to four groups, and asked to write two essays. Groups 1 and 3 received hints before Essay 1, whilst Groups 2 and 4 received hints before Essay 2. Groups 1 and 2 received essential hints; Groups 3 and 4 received helpful hints. Essays were marked against set criteria. The results showed that an “advice for action” approach to essay-writing, in the form of hints, can significantly improve writers’ marks. Specifically higher marks were gained for the introduction, conclusion and use of evidence: critical components of “good” academic essays. As the hints given were content-free, this approach has the potential to instantly benefit tutors and students across subject domains and institutions and is informing the development of a technical system that can offer formative feedback as students draft essays

Industrial structural geology : principles, techniques and integration : an introduction

The authors wish to acknowledge the generous financial support provided in association with this volume to the Geological Society and the Petroleum Group by Badley Geoscience Ltd, BP, CGG Robertson, Dana Petroleum Ltd, Getech Group plc, Maersk Oil North Sea UK Ltd, Midland Valley Exploration Ltd, Rock Deformation Research (Schlumberger) and Borehole Image & Core Specialists (Wildcat Geoscience, Walker Geoscience and Prolog Geoscience). We would like to thank the fine team at the Geological Society’s Publishing House for the excellent support and encouragement that they have provided to the editors and authors of this Special Publication.Peer reviewedPublisher PD

Using student experience as a model for designing an automatic feedback system for short essays

The SAFeSEA project (Supportive Automated Feedback for Short Essay Answers) aims to develop an automated feedback system to support university students as they write summative essays. Empirical studies carried out in the initial phase of the system’s development illuminated students’ approaches to and understandings of the essay-writing process. Findings from these studies suggested that, regardless of their experience of higher education, students consider essay-writing as: 1) a sequential set of activities, 2) a process that is enhanced through particular sources of support and 3) a skill that requires the development of personal strategies. Further data collected from tutors offered insight into the feedback and reflection stages of essay-writing. These perspectives offered a fundamental model of essay-writing and feedback to inform the ongoing, iterative development of this automated feedback system and indeed, for any institution developing tools to support students’ writing

OpenEssayist: real-life testing of an automated feedback system for draft essay writing

OpenEssayist is unique in being an automated feedback system that has been developed to offer feedback on students' draft essays, rather than assessment on their finished work. This is therefore a system that offers opportunities for students to engage with and reflect on their work, and to improve their work through understanding of the requirements of academic essay writing. In trialling use of the system in a genuine Open University course, we found that students made use of it to varying degrees, which is perhaps likely with any study resource. Those who took the time to explore system affordances and what they could be used for however tended to report more positively on its perceived value. From our analysis we were also able to conclude that a significant positive correlation exists in this sample of students between marks on essay 1 and the number of drafts submitted. We could speculate as to what this may mean for this set of students, or more widely, but it seems clear that use of a system such as OpenEssayist has many potential advantages to students and tutors, which will benefit from further research and exploration

What types of essay feedback influence implementation: Structure alone or structure and content?

Students approach educational courses with varying levels of experience and understanding, and so need appropriate support to inform them of expectations and to guide their learning efforts. Feedback is critical in this process, so that learners can gauge how their performance aligns with expectations, and how they can improve their efforts and attainments. This study focused on the effects of providing different types of feedback on participants’ written essays, as well as on participants’ motivations for learning using measures of motivation and self-efficacy. In terms of research questions, it was important to ascertain whether participants performed differently in subsequent essays after receiving feedback on structure alone or on structure and content; whether their self-reported levels of motivation and attitudes to learning were related to essay performance; and whether the difference in type of feedback affected their self-reported levels of motivation and attitudes to learning. Findings revealed no significant difference in marks between those receiving feedback on structure alone and those receiving feedback on structure and content, which is surprising and deserves further exploration. Even so, using feedback to highlight certain structural elements of essay writing can have a lasting positive impact on participants’ future essay performance

OpenEssayist: extractive summarisation and formative assessment of free-text essays

OpenEssayist is a system which is currently under development. It aims to provide an effective automated interactive feedback system that yields an acceptable level of support for university students writing summative essays. The principal natural language processing technique currently employed is extractive summarisation using graph-based ranking algorithms. OpenEssayist will be piloted in September 2013 with Open University UK students following a Master’s course of study

OpenEssayist: an automated feedback system that supports university students as they write summative essays

OpenEssayist is an automated, interactive feedback system designed to provide an acceptable level of support for students as they write essays for summative assessment. There are two main components to the system: (1) a linguistic analysis engine and (2) a web application that generates feedback for students The main pedagogical challenge in the e-assessment of free text is how to provide meaningful “advice for action” in order to support students writing their summative assessments. We have built a first working version of the system in which we use unsupervised graph-based ranking algorithms (following Mihalcea & Tarau, 2005) to automatically extract key words, phrases and sentences from student essays. We have developed several external representations of these summarisation techniques. For examples, key words and key phrases can be viewed in a word cloud or in a dispersion graph, and they can be explored and organised into groups. Holistic approaches have also been tested using ‘mash ups’ where key words and key sentences are highlighted in context in the essay itself, helping students to investigate the distribution of key words and its potential implications for the clarity of the narrative. This paper will report the findings from our pilot studies of the interactive models associated with the summarisation techniques

An exploration of the features of graded student essays using domain-independent natural language processing techniques

This paper presents observations that were made about a corpus of 135 graded student essays by analysing them with a computer program that we are designing to provide automated formative feedback on draft essays. In order to provide individualised feedback to help students to improve their essays, the program carries out automatic essay structure recognition and uses domain-independent graph-based ranking techniques to derive extractive summaries. These procedures generate data concerning an essay’s organisational structure and its discourse structure. We have selected 27 attributes from the data and used them in a comparative analysis of all the essays with a view to informing further development of the feedback program. The results of this analysis suggest that some characteristics of students’ essays that our domain-independent feedback program is measuring may be related to the grades that tutors assign to their essays

Designing and testing visual representations of draft essays for higher education students

This paper reports the findings of an empirical investigation, which set out to test a set of rainbow essay exercises. The rainbow diagrams are pictorial representations of formal graphs that are derived automatically from student essays. They were designed to allow students to discover how key concepts in a well written essay are connected together. The students would then be able to compare a rainbow diagram of their own essay with a good essay and make changes to it before submission to their tutor. However a trail was undertaken with academics, teaching and learning staff, doctoral students at the Open University of Catalonia and the Open University UK, before implementation into the web application known as Open Essayist. All the participants from each University completed the exercise correctly. This was a surprising finding as we expected participants to experience some difficulties, as previous visual representations we piloted. All the participants remarked that they had learnt a lot about the structure of good essays and more importantly how clear the role of the conclusion played in a well-constructed essay. This type of representation made this explicit and they would be able to see quickly if a second draft had improved. The users also mentioned that the rainbow diagram representations could be used as a generic essay feedback tool. It could be used across subject domains, a hypothesis worthy of further investigation