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Correlations between automated rhetorical analysis and tutors' grades on student essays
Abstract
When assessing student essays, educators look for the students’
ability to present and pursue well-reasoned and strong arguments.
Such scholarly argumentation is often articulated by rhetorical
metadiscourse. Educators will be necessarily examining
metadiscourse in students’ writing as signals of the intellectual
moves that make their reasoning visible. Therefore students and
educators could benefit from available powerful automated
textual analysis that is able to detect rhetorical metadiscourse.
However, there is a need to validate such technologies in higher
education contexts, since they were originally developed in noneducational
applications. This paper describes an evaluation study
of a particular language analysis tool, the Xerox Incremental
Parser (XIP), on undergraduate social science student essays,
using the mark awarded as a measure of the quality of the writing.
As part of this exploration, the study presented in this paper seeks
to assess the quality of the XIP through correlational studies and
multiple regression analysis.
... Several works proposed the adoption of shallow features, as word counts, to assess essays (Dikli, 2006;Rudner, Garcia, & Welch, 2006). However, it is important to go beyond this analysis (Crossley et al., 2015;Ericsson & Haswell, 2006) and there are a great number of papers focused on adoption of semantic methods (Hughes, Hastings, Magliano, Goldman, & Lawless, 2012;Simsek et al., 2015), writing style (Oberreuter & Velásquez, 2013;Snow, Allen, Jacovina, Perret, & McNamara, 2015) and argumentation analysis Elouazizi et al. (2017).Following a similar direction, the evaluation of online assignments adopts lexical and semantic approaches (Cutrone & Chang, 2010;Prevost, Haudek, Urban-Lurain, & Merrill, 2012;Ramachandran & Gehringer, 2011). Nevertheless, in this case, the works tend to be more focus on solving specific problems as plagiarism (Adeva, Carroll, & Calvo, 2006), analyze short answer (Saha, Dhamecha, Marvaniya, Sindhgatta, & Sengupta, 2018), and classify the questions (Godea, Tulley-Patton, Barbee, & Nielsen, 2018). ...
... This study utilized automated textual analyses to examine potential misalignments between students' and instructors' evaluation criteria for writing quality. (Simsek et al., 2015) Essay NLP Evaluation This paper describes an evaluation study of a particular language analysis tool, the Xerox incremental parser (XIP), on undergraduate social science student essays. ...
The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends.
This article is categorized under: Application Areas > Education and Learning
Ensemble Methods > Text Mining
... 3 Usability aside, the next question was whether AWA's output was experienced as academically trustworthy by the civil law lecturer, and her students. To date, we have reported statistical correlations between the frequency of certain XIP classifications and the quality of English literature essays (Simsek et al. 2015). However, user experience testing has not yet been reported; this application to the legal domain provides a first step to roll-out to students within a single domain. ...
... BDoes this Highlighting Mean it's Good?R elated to the previous point, but standing as a question in its own right is the extent to which students and educators should be encouraged to use rhetorically-based highlighting as proxies for the overall quality of the piece. Prior work (Simsek et al. 2015) has investigated statistical relationships between the frequency of all or particular XIP sentence types, and essay grade, with some positive correlations found, but clearly there is much more to the creation of a coherent piece of writing than just this indicator, so one does not expect it to account for all variance. Rhetorical parsing on its own does not assess the truth or internal consistency of statements (for which fact-checking or domain-specific ontology-based annotation (Cohen and Hersh 2005) could be used). ...
Research into the teaching and assessment of student writing shows that many students find academic writing a challenge to learn, with legal writing no exception. Improving the availability and quality of timely formative feedback is an important aim. However, the time-consuming nature of assessing writing makes it impractical for instructors to provide rapid, detailed feedback on hundreds of draft texts which might be improved prior to submission. This paper describes the design of a natural language processing (NLP) tool to provide such support. We report progress in the development of a web application called AWA (Academic Writing Analytics), which has been piloted in a Civil Law degree. We describe: the underlying NLP platform and the participatory design process through which the law academic and analytics team tested and refined an existing rhetorical parser for the discipline; the user interface design and evaluation process; and feedback from students, which was broadly positive, but also identifies important issues to address. We discuss how our approach is positioned in relation to concerns regarding automated essay grading, and ways in which AWA might provide more actionable feedback to students. We conclude by considering how this design process addresses the challenge of making explicit to learners and educators the underlying mode of action in analytic devices such as our rhetorical parser, which we term algorithmic accountability.
... A large body of work has also examined the association between different qualities of student essays and performance. The primary goal of these studies is to understand what encompasses successful writing (Allen, Snow, & McNamara, 2014;Crossley, Roscoe, & McNamara, 2014;McNamara, Crossley, & McCarthy, 2009;Snow, Allen, Jacovina, Perret, & McNamara, 2015), and how it relates to course performance (Robinson, Navea, & Ickes, 2013;Simsek et al., 2015). Current research has also revealed direct links between the coherence of the provided learning materials and the quality of students' reading summaries (Allen, . ...
... • Discourse analytics (Simsek et al., 2015(Simsek et al., , 2014(Simsek et al., , 2013, ...
The field of learning analytics recently attracted attention from educational practitioners and researchers interested in the use of large amounts of learning data for understanding learning process and improving learning and teaching practices. In this chapter, we introduce content analytics – a particular form of learning analytics focused on the analysis of different forms of content related to learning. While several publications provided brief overviews of content analytics, the goal of this chapter is to define content analytics and provide a comprehensive overview of the most important studies in the published literature to date. Given the early stage of the learning analytics field, the focus of this chapter is on the important problems and challenges for which existing content analytics approaches are suitable and have been successfully used in the past. We also reflect on the current trends in content analytics and their position within a broader domain of educational research.
... (Knight et al., 2018, p. 5) Prior work using the XIP rhetorical parser indicated that the detection of the rhetorical steps mentioned above could be used to identify "paradigm shifts" in biomedical research abstracts (Lisacek, Chichester, Kaplan, & Sándor, 2005), and that it could effectively support peer reviewers (Sándor & Vorndran, 2009) and project evaluators (De Liddo, Sándor, & Buckingham Shum, 2012) in navigating the research they were reviewing. In addition, Simsek et al. (2015) suggested that the presence of these steps had some correlation with undergraduate essay quality. This evidence established the potential of the XIP rhetorical parser for annotating rhetorical steps in academic writing and led to its application in the AWA tool as a hosted service. ...
Writing analytics has emerged as a sub-field of learning analytics, with applications including the provision of formative feedback to students in developing their writing capacities. Rhetorical markers in writing have become a key feature in this feedback, with a number of tools being developed across research and teaching contexts. However, there is no shared corpus of texts annotated by these tools, nor is it clear how the tool annotations compare. Thus, resources are scarce for comparing tools for both tool development and pedagogic purposes. In this paper, we conduct such a comparison and introduce a sample corpus of texts representative of the particular genres, a subset of which has been annotated using three rhetorical analysis tools (one of which has two versions). This paper aims to provide both a description of the tools and a shared dataset in order to support extensions of existing analyses and tool design in support of writing skill development. We intend the description of these tools, which share a focus on rhetorical structures, alongside the corpus, to be a preliminary step to enable further research, with regard to both tool development and tool interaction
... The student writing used for generating the XIP analysis to be used in the dashboard came from an education and arts module at The Open University, UK (OU). The reason for selecting this module was because of the previous work [6], which demonstrated the relation of students' essay marks with the XIP analysis, and produced promising results about the XIP's performance students' essays that were in line with tutors' marking rubrics. ...
Effective written communication is an essential skill which promotes educational success for undergraduates. However, undergraduate students, especially those in their first year at university, are unused to this form of writing. After their long experience with the schoolroom essay, for most undergraduates academic writing development is painstakingly slow. Thus, especially those with poor writing abilities, should write more to be better writers. Yet, the biggest impediment to more writing is that overburdened tutors would ask limited number of drafts from their students. Today, there exist powerful computational language technologies that could evaluate student writing, saving time and providing timely, speedy, reliable feedback which can support educators marking process. This paper motivates an updated visual analytics dashboard, XIPIt, to introduce a set of visual and writing analytics features embedded in a marking environment built on XIP output.
... This was to help students understand the role of rhetorical structures and the usage of automated feedback on them in a way that it can be applied for their subject essay writing. These rhetorical structures guide the reader through the argument structure of a text, and are a key part of academic writing (Hyland, 2005), with their presence having some (small) relationship to essay quality (Simsek et al., 2015). An intervention grounded by pedagogy was designed for students to learn essay writing and revision skills based on rhetorical moves in the context of their subject curriculum by augmenting existing practice with learning analytics. ...
Academic writing can be supported by the provision of formative feedback in many forms including instructor feedback, peer feedback and automated feedback. However, for these feedback types to be effective, they should be applied in well-designed pedagogic contexts. In my pilot study, automated feedback from a writing analytics tool has been implemented in pedagogic interventions, which integrate learning analytics technologies to existing practice in an educational context. To improve the learning design and to study the use of human insights in this context, a peer discussion module is planned to be added. This kind of peer discussion can augment automated feedback applications by making students aware of the limitations of such artificial intelligence powered feedback, and develop writing literacy by providing additional contextual feedback for their peers. The learning analytics intervention design when tested across different disciplines can validate the usefulness of this approach to improve students' academic writing in authentic pedagogic contexts. The design can be implemented using a learning analytics tool which is developed to facilitate the intervention and provide analytic capabilities by collecting learner data.
... (Holman, Aguilar, y Fishman, 2013), (Øhrstrøm, Sandborg-Petersen, Thorvaldsen, y Ploug, 2013) y (Westera, Nadolski, y Hummel, 2013) Aprendizaje de máquinas Encontrar ideas ocultas en datos automáticamente (basado en modelos que se exponen a nuevos datos y se adaptan independientemente). (Corrigan, Smeaton, Glynn, y Smyth, 2015), (McKay, Miller, y Tritz, 2012) y (Nespereira, Elhariri, El-Bendary, Vilas, y Redondo, 2016) Estadística Análisis e interpretación de datos cuantitativos para la toma de decisiones. (Clow, 2014), (Khousa y Atif, 2014) y (Simsek et al., 2015) Fuente: elaboración propia REVIS Árboles/reglas de decisión: método supervisado (hay un conocimiento a priori) y no paramétrico, utilizado en el aprendizaje de clasificación y regresión. Permite crear modelos para predecir el valor de una variable de destino mediante el aprendizaje de reglas de decisión simples, inferidas a partir de las características de los datos (Scikit-Learn, 2017). ...
The majority of algorithms used in the data analysis are designed according to the capacities of power and flexibility rather than for its simplicity and are too complex to use in the educational context. The objective of this paper is to present a literature review on the learning analytics in higher education: problems, limitations, techniques and tools used. The systematic literature review methodology was used to answer three research questions on the basis of scientific publications. This paper concludes that it must implement, adapt or develop algorithms for the educational context and must be build tools for the analysis of educational data with intuitive interfaces and easy to use.
... AWA uses natural language processing techniques to identify sentences in a text that match specific rhetorical functions, like emphasizing an important point or summarizing, by using linguistic markers that indicate these rhetorical moves (Knight et al., 2016). These kinds of moves are a key component in good academic writing and are seen to be correlated to essay quality (Simsek et al., 2015). Feedback on the presence of these moves should help students reflect on their writing and the rhetorical structure of it. ...
Academic writing is a key skill required for higher education students, which is often challenging to learn. A promising approach to help students develop this skill is the use of automated tools that provide formative feedback on writing. However, such tools are not widely adopted by students unless useful for their discipline-related writing, and embedded in the curriculum. This recognition motivates an increased emphasis in the field on aligning learning analytics applications with learning design, so that analytics-driven feedback is congruent with the pedagogy and assessment regime. This paper describes the design, implementation, and evaluation of a pedagogic intervention that was developed for law students to make use of an automated Academic Writing Analytics tool (AWA) for improving their academic writing. In exemplifying this pedagogically aligned learning analytic intervention, we describe the development of a learning analytics platform to support the pedagogic design, illustrating its potential through example analyses of data derived from the task.
... Simsek, Buckingham Shum, Sándor, De Liddo, and Ferguson (2013) provide a more detailed rationale for the use of the analytical writing parser in education, and the description of a prototype dashboard. Simsek et al. (2015) report a preliminary evaluation in the context of an English literature student assignment, and Knight, Buckingham Shum, Ryan, Sándor, and Wang (in press) report a more detailed evaluation with Civil Law students. The reflective writing parser documented in this paper is an extension of this XIP module. ...
When used effectively, reflective writing tasks can deepen learners’ understanding of key concepts, help them critically appraise their developing professional identity, and build qualities for lifelong learning. As such, reflecting writing is attracting substantial interest from universities concerned with experiential learning, reflective practice, and developing a holistic conception of the learner. However, reflective writing is for many students a novel genre to compose in, and tutors may be inexperienced in its assessment. While these conditions set a challenging context for automated solutions, natural language processing may also help address the challenge of providing real time, formative feedback on draft writing. This paper reports progress in designing a writing analytics application, detailing the methodology by which informally expressed rubrics are modelled as formal rhetorical patterns, a capability delivered by a novel web application. Preliminary tests on an independently human-annotated corpus are encouraging, showing improvements from the first to second version, but with much scope for improvement. We discuss a range of issues: the prevalence of false positives in the tests, areas for futures technical improvements, the risks of gaming the system, and the participatory design process that has enabled work across disciplinary boundaries to develop the prototype to its current state.
... (Corrigan et al. 2015;McKay et al. 2012;Nespereira et al. 2016) Statistic Analysis and interpretation of quantitative data for decision making. (Clow 2014;Khousa and Atif 2014;Simsek et al. 2015) The results of Fig. 3.5 show, that the research is focused mainly on prediction with a total of 36 citations. Outlier detection for pointing out at-risk or dropping out students with a citation count of 29. ...
This chapter looks into examining research studies of the last five years and presents the state of the art of Learning Analytics (LA) in the Higher Education (HE) arena. Therefore, we used mixed-method analysis and searched through three popular libraries, including the Learning Analytics and Knowledge (LAK) conference, the SpringerLink, and the Web of Science (WOS) databases. We deeply examined a total of 101 papers during our study. Thereby, we are able to present an overview of the different techniques used by the studies and their associated projects. To gain insights into the trend direction of the different projects, we clustered the publications into their stakeholders. Finally, we tackled the limitations of those studies and discussed the most promising future lines and challenges. We believe the results of this review may assist universities to launch their own LA projects or improve existing ones.
Effective written communication is an essential skill which promotes educational success for undergraduates. One of the key requirements of good academic writing in higher education is that students must develop a critical mind and learn how to construct sound arguments in their discipline. Writing analytics focuses on the measurement and analysis of written texts to improve the teaching and learning of writing and is being developed at the intersection of fields such as automated assessment and computational linguistics. Since writing is an activity that is deeply human, its association with computational formulations is double-edged. This chapter discusses issues and challenges for implementing writing analytics in higher education through theoretical considerations that emerge from the literature review and an example application. It includes findings from empirical research conducted with academic tutors of the Open University, UK, on adopting writing analytics to support their feedback processes, which reveal the preconceptions that academic tutors have had about the use of writing analytics specifically concerns centred around the privacy and ethical aspects.
There is an increasing interest in the analysis of both student's writing and the temporal aspects of learning data. The analysis of higher-level learning features in writing contexts requires analyses of data that could be characterised in terms of the sequences and processes of textual features present. This paper (1) discusses the extant literature on sequential and process analyses of writing; and, based on this and our own first-hand experience on sequential analysis, (2) proposes a number of approaches to both pre-process and analyse sequences in whole-texts. We illustrate how the approaches could be applied to examples drawn from our own datasets of 'rhetorical moves' in written texts, and the potential each approach holds for providing insight into that data. Work is in progress to apply this model to provide empirical insights. Although, similar sequence or process mining techniques have not yet been applied to student writing, techniques applied to event data could readily be operationalised to undercover patterns in texts.
Writing Analytics focuses on the measurement and analysis of written texts for the purpose of understanding writing processes and products, in their educational contexts, and improving the teaching and learning of writing. This workshop adopts a critical, holistic perspective in which the definition of "the system" and "success" is not restricted to IR metrics such as precision and recall, but recognizes the many wider issues that aid or obstruct analytics adoption in educational settings, such as theoretical and pedagogical grounding, usability, user experience, stakeholder design engagement, practitioner development, organizational infrastructure, policy and ethics.
I am grateful to Routledge, New York, for permission to include the Contents, opening chapter and References. The book is based on a number of research projects, and argues the case for a more balanced deployment of argumentation within higher education internationally. That balance will be achieved between generic teaching of argumentational skills on the one hand, and discipline-specific argumentational skills on the other.
Learning Analytics, an emerging field concerned with analyzing the vast data "given off" by learners in technology supported settings to inform educational theory and practice, has from its inception taken a multidisciplinary approach that integrates studies of learning with technological capabilities. In this introduction to the Proceedings of the Third International Learning Analytics & Knowledge Conference, we discuss how Learning Analytics must function in the "middle space" where learning and analytic concerns meet. Dialogue in this middle space involves diverse stakeholders from multiple disciplines with various conceptions of the agency and nature of learning. We hold that a singularly unified field is not possible nor even desirable if we are to leverage the potential of this diversity, but progress is possible if we support "productive multivocality" between the diverse voices involved, facilitated by appropriate use of boundary objects. We summarize the submitted papers and contents of these Proceedings to characterize the voices and topics involved in the multivocal discourse of Learning Analytics.
This paper describes a novel analytics dashboard which visualises the key features of scholarly documents. The Dashboard aggregates the salient sentences of scholarly papers, their rhetorical types and the key concepts mentioned within these sentences. These features are extracted from papers through a Natural Language Processing (NLP) technology, called Xerox Incremental Parser (XIP). The XIP Dashboard is a set of visual analytics modules based on the XIP output. In this paper, we briefly introduce the XIP technology and demonstrate an example visualisation of the XIP Dashboard.
Drawing on sociological and philosophical studies of science, science educators have begun to view argumentation as a central scientific practice that students should learn. In this article, we extend recent work to understand the structure of students' arguments to include judgments about their quality through content analyses of high school students' written explanations for 2 problems of natural selection. In these analyses, we aim to explicate the relations between students' conceptual understanding of specific domains and their epistemic understanding of scientific practices of argumentation as they try to learn science through inquiry. We present a method that assesses the warrant of explanatory claims, the sufficiency of the evidence explicitly cited for claims, and students' rhetorical use of specific inscriptions in their arguments. Students were attentive to the need to cite data, yet they often failed to cite sufficient evidence for claims. Students' references to specific inscriptions in their arguments often failed to articulate how specific data related to particular claims. We discuss these patterns of data citation in terms of what they suggest about students' epistemological ideas about explanation and consequent implications for inquiry-oriented, science education reforms.
Undergraduate students are commonly advised to make plans of their essays before they begin writing, yet there is little empirical evidence on the nature, role or efficacy of essay planning. This paper examines findings from a recent study of coursework essay‐writing as an aspect of student learning in the social sciences. A total of 16 Psychology and 17 History students took part in the investigation, which mainly took the form of two sets of semi‐structured interviews. From an analysis of the interview transcripts, five planning strategies were identified. However, it was apparent that to gain a full understanding of the nature of planning, it was necessary to examine these strategies in the light of the main findings of the study—a qualitative analysis of differences in the students’ conceptions of what an essay was and what essay‐writing involved. In sum, what seemed important was less how a student planned than what planning was directed towards.
Robustness is a key issue for natural language processing in general and parsing in particular,
and many approaches have been explored in the last decade for the design of robust parsing
systems. Among those approaches is shallow or partial parsing, which produces minimal and
incomplete syntactic structures, often in an incremental way. We argue that with a systematic
incremental methodology one can go beyond shallow parsing to deeper language analysis,
while preserving robustness. We describe a generic system based on such a methodology and
designed for building robust analyzers that tackle deeper linguistic phenomena than those
traditionally handled by the now widespread shallow parsers. The rule formalism allows the
recognition of n-ary linguistic relations between words or constituents on the basis of global
or local structural, topological and/or lexical conditions. It offers the advantage of accepting
various types of inputs, ranging from raw to chunked or constituent-marked texts, so for
instance it can be used to process existing annotated corpora, or to perform a deeper analysis
on the output of an existing shallow parser. It has been successfully used to build a deep
functional dependency parser, as well as for the task of co-reference resolution, in a modular
way.
We present an approach to knowledge discovery based on the author's comments on pieces of in-formation he conveys in his text. To carry out this task we propose a pattern-matching framework based on syntactic analysis that is able to repre-sent a large variety of expressions that convey the same comment type. Our method has been applied effectively in novelty detection and risk detection tasks. We propose this approach and framework as a complementary methodology of information ex-traction and subsequent text-mining.
Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of ‘hypotheses and evidence’. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area.
In this paper, we focus on the 'problem' of student writing in higher education. We set out to explore this problem from two perspectives: first, from the perspective of 'non-traditional' student-writers as they attempt to engage in academic writing and, second, from the perspective of a cultural-historical tradition of scientific rationality. A common frame of reference for these perspectives we see as a 'discourse of transparency', whereby language is treated as ideally transparent and autonomous. We illustrate how this discourse of transparency is currently enacted and historically situated. We argue that current academic practices need to be located within a broader historical and epistemological framework both in order to reach a deeper understanding about what's involved in student writing and in order to inform meaningful pedagogies.
Student academic writing is at the heart of teaching and learning in higher education. Students are assessed largely by what they write, and need to learn both general academic conventions as well as disciplinary writing requirements in order to be successful in higher education. Teaching Academic Writing is a 'toolkit' designed to help higher education lecturers and tutors teach writing to their students. Containing a range of diverse teaching strategies, the book offers both practical activities to help students develop their writing abilities and guidelines to help lecturers and tutors think in more depth about the assessment tasks they set and the feedback they give to students. The authors explore a wide variety of text types, from essays and reflective diaries to research projects and laboratory reports. The book draws on recent research in the fields of academic literacy, second language learning, and linguistics. It is grounded in recent developments such as the increasing diversity of the student body, the use of the Internet, electronic tuition, and issues related to distance learning in an era of increasing globalisation. Written by experienced teachers of writing, language, and linguistics, Teaching Academic Writing will be of interest to anyone involved in teaching academic writing in higher education.
Integrating technology into higher education curricula to extend the physical boundaries of the classroom can be of significant value, as it enables students to interact and learn outside of class time. This is particularly true in larger gateway courses, where there are fewer opportunities for students to engage in higher-order thinking and to construct their own understanding of core concepts. While the introduction of technology like blogging can create a successful learning experience, any large number of students creates additional noise that makes it harder for instructors to isolate the students most in need of help. This chapter describes an analysis method and tool that uses lexical analysis to allow an instructor to automatically track how a student’s written language migrates from mere paraphrase to mastery, isolating the moment when the student’s understanding of core concepts best demonstrates an ability to place that concept into his or her own words, a moment that we have chosen to call the "Point of Originality." Through two case studies, higher point of originality values is shown to correspond strongly with likely student achievement.
I am grateful to Routledge, New York, for permission to include the Contents, opening chapter and References. The book is based on a number of research projects, and argues the case for a more balanced deployment of argumentation within higher education internationally. That balance will be achieved between generic teaching of argumentational skills on the one hand, and discipline-specific argumentational skills on the other.
A key step in helping students to achieve scientific literacy is to ensure that each school's curriculum supports students' efforts to learn science meaningfully. Educational researchers play a vital role in this step by providing teachers, teacher educators, administrators, and policy makers with information about the creation of a curriculum that supports scientific literacy. In a scientific literacy curriculum, reading and writing can serve as dynamic vehicles for learning science meaningfully. The task of educational researchers is to show how reading and writing can be used most effectively to support science learning. Much of what is done now in schools is based on teacher intuition—good intuition—but intuition nonetheless. What is needed is school-based research to validate and build upon these intuitions. This article is intended to stimulate research on reading and writing to learn science.
This study uses objective measures from essay scripts to report on the interaction between students' strategies in essay writing and tutors' strategies in marking. 98 1st year psychology students completed questionnaires on their essay writing strategies and submitted their essays for analysis. Six tutors who marked the essays were subsequently interviewed. The results indicate that the amount of time spent on the essay, the number of books used, the number of references cited and proportion of research based content actually found in the essays were all strategies that led to higher essay marks. However, there was clear evidence of a mismatch between students and tutors on their perceptions of the most important criteria - students were more concerned with content, whereas tutors were more concerned with argument.
We present an automated approach to classify sentences of scholarly work with respect to their rhetorical function. While previous work that achieves this task of argumentative zoning requires richly annotated input, our approach is robust to noise and can process raw text. Even in cases where the input has noise (as it is obtained from optical character recognition or text extraction from PDF files), our robust classifier is largely accurate. We perform an in-depth study of our system both with clean and noisy inputs. We also give preliminary results from in situ acceptability testing when the classifier is embedded within a digital library reading environment.
This article addresses the issue of student writing in higher education. It draws on the findings of an Economic and Social Research Council funded project which examined the contrasting expectations and interpretations of academic staff and students regarding undergraduate students" written assignments. It is suggested that the implicit models that have generally been used to understand student writing do not adequately take account of the importance of issues of identity and the institutional relationships of power and authority that surround, and are embedded within, diverse student writing practices across the university. A contrasting and therefore complementary perspective is used to present debates about "good" and "poor' student writing. The article outlines an 'academic literacies' framework which can take account of the conflicting and contested nature of writing practices, and may therefore be more valuable for understanding student writing in today's higher education than traditional models and approaches.
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