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Automated Essay Scoring Systems

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Essays are scholarly compositions with a specific focus on a phenomenon in question. They provide learners the opportunity to demonstrate in-depth understanding of a subject matter; however, evaluating, grading, and providing feedback on written essays are time consuming and labor intensive. Advances in automated assessment systems may facilitate the feasibility, objectivity, reliability, and validity of the evaluation of written prose as well as providing instant feedback during learning processes. Measurements of written text include observable components such as content, style, organization, and mechanics. As a result, automated essay scoring systems generate a single score or detailed evaluation of predefined assessment features. This chapter describes the evolution and features of automated scoring systems, discusses their limitations, and concludes with future directions for research and practice.

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• Automated essay scoring
• Essay grading system
• Writing assessment
• Natural language processing
• Educational measurement
• Technology-enhanced assessment
• Automated writing evaluation

Introduction

Educational assessment is a systematic method of gathering information or artifacts about a learner and learning processes to draw inferences of the persons’ dispositions (E. Baker, Chung, & Cai, 2016 ). Various forms of assessments exist, including single- and multiple-choice, selection/association, hot spot, knowledge mapping, or visual identification. However, using natural language (e.g., written prose or essays) is regarded as the most useful and valid technique for assessing higher-order learning processes and learning outcomes (Flower & Hayes, 1981 ). Essays are scholarly analytical or interpretative compositions with a specific focus on a phenomenon in question. Valenti, Neri, and Cucchiarelli ( 2003 ) as well as Zupanc and Bosnic ( 2015 ) note that written essays provide learners the opportunity to demonstrate higher order thinking skills and in-depth understanding of a subject matter. However, evaluating, grading, and providing feedback on written essays are time consuming, labor intensive, and possibly biased by an unfair human rater.

For more than 50 years, the concept of developing and implementing computer-based systems, which may support automated assessment and feedback of written prose, has been discussed (Page, 1966 ). Technology-enhanced assessment systems enriched standard or paper-based assessment approaches, some of which hold much promise for supporting learning processes and learning outcomes (Webb, Gibson, & Forkosh-Baruch, 2013 ; Webb & Ifenthaler, 2018 ). While much effort in institutional and national systems is focused on harnessing the power of technology-enhanced assessment approaches in order to reduce costs and increase efficiency (Bennett, 2015 ), a range of different technology-enhanced assessment scenarios have been the focus of educational research and development, however, often at small scale (Stödberg, 2012 ). For example, technology-enhanced assessments may involve a pedagogical agent for providing feedback during a learning process (Johnson & Lester, 2016 ). Other scenarios of technology-enhanced assessments include analyses of a learners’ decisions and interactions during game-based learning (Bellotti, Kapralos, Lee, Moreno-Ger, & Berta, 2013 ; Kim & Ifenthaler, 2019 ), scaffolding for dynamic task selection including related feedback (Corbalan, Kester, & van Merriënboer, 2009 ), remote asynchronous expert feedback on collaborative problem-solving tasks (Rissanen et al., 2008 ), or semantic rich and personalized feedback as well as adaptive prompts for reflection through data-driven assessments (Ifenthaler & Greiff, 2021 ; Schumacher & Ifenthaler, 2021 ).

It is expected that such technology-enhanced assessment systems meet a number of specific requirements, such as (a) adaptability to different subject domains, (b) flexibility for experimental as well as learning and teaching settings, (c) management of huge amounts of data, (d) rapid analysis of complex and unstructured data, (e) immediate feedback for learners and educators, as well as (f) generation of automated reports of results for educational decision-making.

Given the on-going developments in computer technology, data analytics, and artificial intelligence, there are advances in automated assessment systems, which may facilitate the feasibility, objectivity, reliability, and validity of the assessment of written prose as well as providing instant feedback during learning processes (Whitelock & Bektik, 2018 ). Accordingly, automated essay grading (AEG) systems, or automated essay scoring (AES systems, are defined as a computer-based process of applying standardized measurements on open-ended or constructed-response text-based test items. Measurements of written text include observable components such as content, style, organization, mechanics, and so forth (Shermis, Burstein, Higgins, & Zechner, 2010 ). As a result, the AES system generates a single score or detailed evaluation of predefined assessment features (Ifenthaler, 2016 ).

This chapter describes the evolution and features of automated scoring systems, discusses their limitations, and concludes with future directions for research and practice.

Synopsis of Automated Scoring Systems

The first widely known automated scoring system, Project Essay Grader (PEG), was conceptualized by Ellis Battan Page in late 1960s (Page, 1966 , 1968 ). PEG relies on proxy measures, such as average word length, essay length, number of certain punctuation marks, and so forth, to determine the quality of an open-ended response item. Despite the promising findings from research on PEG, acceptance and use of the system remained limited (Ajay, Tillett, & Page, 1973 ; Page, 1968 ). The advent of the Internet in the 1990s and related advances in hard- and software introduced a further interest in designing and implementing AES systems. The developers primarily aimed to address concerns with time, cost, reliability, and generalizability regarding the assessment of writing. AES systems have been used as a co-rater in large-scale standardized writing assessments since the late 1990s (e.g., e-rater by Educational Testing Service). While initial systems focused on English language, a wide variety of languages have been included in further developments, such as Arabic (Azmi, Al-Jouie, & Hussain, 2019 ), Bahasa Malay (Vantage Learning, 2002 ), Hebrew (Vantage Learning, 2001 ), German (Pirnay-Dummer & Ifenthaler, 2011 ), or Japanese (Kawate-Mierzejewska, 2003 ). More recent developments of AES systems utilize advanced machine learning approaches and elaborated natural language processing algorithms (Glavas, Ganesh, & Somasundaran, 2021 ).

For almost 60 years, different terms related to automated assessment of written prose have been used mostly interchangeably. Most frequently used terms are automated essay scoring (AES) and automated essay grading (AEG); however, more recent research used the term automated writing evaluation (AWE) and automated essay evaluation (AEE) (Zupanc & Bosnic, 2015 ). While the above-mentioned system focuses on written prose including several hundred words, another field developed focusing on short answers referred to as automatic short answer grading (ASAG) (Burrows, Gurevych, & Stein, 2015 ).

Functions of Automated Scoring Systems

AES systems mimic human evaluation of written prose by using various methods of scoring, that is, statistics, machine learning, and natural language processing (NLP) techniques. Implemented features of AES systems vary widely, yet they are mostly trained with large sets of expert-rated sample open-ended assessment items to internalize features that are relevant to human scoring. AES systems compare the features in training sets to those in new test items to find similarities between high/low scoring training and high/low scoring new ones and then apply scoring information gained from training sets to new item responses (Ifenthaler, 2016 ).

The underlying methodology of AES systems varies; however, recent research mainly focuses on natural language processing approaches (Glavas et al., 2021 ). AES systems focusing on content use Latent Semantic Analysis (LSA) which assumes that terms or words with similar meaning occur in similar parts of written text (Wild, 2016 ). Other content-related approaches include Pattern Matching Techniques (PMT). The idea of depicting semantic structures, which include concepts and relations between the concepts, has its source in two fields: semantics (especially propositional logic) and linguistics. Semantic oriented approaches include Ontologies and Semantic Networks (Pirnay-Dummer, Ifenthaler, & Seel, 2012 ). A semantic network represents information in terms of a collection of objects (nodes) and binary associations (directed labeled edges), the former standing for individuals (or concepts of some sort), and the latter standing for binary relations over these. Accordingly, a representation of knowledge in a written text by means of a semantic network corresponds with a graphical representation where each node denotes an object or concept, and each labeled being one of the relations used in the knowledge representation. Despite the differences between semantic networks, three types of edges are usually contained in all network representation schemas (Pirnay-Dummer et al., 2012 ): (a) Generalization: connects a concept with a more general one. The generalization relation between concepts is a partial order and organizes concepts into a hierarchy. (b) Individualization: connects an individual (token) with its generic type. (c) Aggregation: connects an object with its attributes (parts, functions) (e.g., wings – part of – bird). Another method of organizing semantic networks is partitioning which involves grouping objects and elements or relations into partitions that are organized hierarchically, so that if partition A is below partition B, everything visible or present in B is also visible in A unless otherwise specified (Hartley & Barnden, 1997 ).

From an information systems perspective, understood as a set of interrelated components that accumulate, process, store, and distribute information to support decision making, several preconditions and processes are required for a functioning AES system (Burrows et al., 2015 ; Pirnay-Dummer & Ifenthaler, 2010 ):

Assessment scenario: The assessment task with a specific focus on written prose needs to be designed and implemented. Written text is being collected from learners and from experts (being used as a reference for later evaluation).

Preparation: The written text may contain characters which could disturb the evaluation process. Thus, a specific character set is expected. All other characters may be deleted. Tags may be also deleted, as are other expected metadata within each text.

Tokenizing: The prepared text gets split into sentences and tokens. Tokens are words, punctuation marks, quotation marks, and so on. Tokenizing is somewhat language dependent, which means that different tokenizing methods are required for different languages.

Tagging: There are different approaches and heuristics for tagging sentences and tokens. A combination of rule-based and corpus-based tagging seems most feasible when the subject domain of the content is unknown to the AES system. Tagging and the rules for it is a quite complex field of linguistic methods (Brill, 1995 ).

Stemming: Specific assessment attributes may require that flexions of a word will be treated as one (e.g., the singular and plural forms “door” and “doors”). Stemming reduces all words to their word stems.

Analytics: Using further natural language processing (NLP) approaches, the prepared text is analyzed regarding predefined assessment attributes (see below), resulting in models and statistics.

Prediction: Further algorithms produce scores or other output variables based on the analytics results.

Veracity: Based on available historical data or reference data, the analytics scores are compared in order to build trust and validity in the AES result.

Common assessment attributes of AES have been identified by Zupanc and Bosnic ( 2017 ) including linguistic (lexical, grammar, mechanics), style, and content attributes. Among 28 lexical attributes, frequencies of characters, words, sentences are commonly used. More advanced lexical attributes include average sentence length, use of stopwords, variation in sentence length, or the variation of specific words. Other lexical attributes focus on readability or lexical diversity utilizing specific measures such as Gunning Fox index, Nominal ratio, Type-token-ratio (DuBay, 2007 ). Another 37 grammar attributes are frequently implemented, such as number of grammar errors, complexity of sentence tree structure, use of prepositions and forms of adjectives, adverbs, nouns, verbs. A few attributes focus on mechanics, for example, the number of spellchecking errors, the number of capitalization errors, or punctuation errors. Attributes that focus on content include similarities with source or reference texts or content-related patterns (Attali, 2011 ). Specific semantic attributes have been described as concept matching and proposition matching (Ifenthaler, 2014 ). Both attributes are based on similarity measures (Tversky, 1977 ). Concept matching compares the sets of concepts (single words) within a written text to determine the use of terms. This measure is especially important for different assessments which operate in the same domain. Propositional matching compares only fully identical propositions between two knowledge representations. It is a good measure for quantifying complex semantic relations in a specific subject domain. Balanced semantic matching measure uses both concepts and propositions to match the semantic potential between the knowledge representations. Such content or semantic oriented attributes focus on the correctness of content and its meaning (Ifenthaler, 2014 ).

Overview of Automated Scoring Systems

Instructional applications of automated scoring systems are developed to facilitate the process of scoring and feedback in writing classrooms. These AES systems mimic human scoring by using various attributes; however, implemented attributes vary widely.

The market of commercial and open-source AES systems has seen a steady growth since the introduction of PEG. The majority of available AES systems extract a set of attributes from written prose and analyze it using some algorithm to generate a final output. Several overviews document the distinct features of AES systems (Dikli, 2011 ; Ifenthaler, 2016 ; Ifenthaler & Dikli, 2015 ; Zupanc & Bosnic, 2017 ). Burrows et al. ( 2015 ) identified five eras throughout the almost 60 years of research in AES: (1) concept mapping, (2) information extraction, (3) corpus-based methods, (4) machine learning, and (5) evaluation.

Zupanc and Bosnic ( 2017 ) note that four commercial AES systems have been predominant in application: PEG, e-rater, IEA, and IntelliMetric. Open access or open code systems have been available for research purposes (e.g., AKOVIA); however, they are yet to be made available to the general public. Table 1 provides an overview of current AES systems, including a short description of the applied assessment methodology, output features, information about test quality, and specific requirements. The overview is far from being complete; however, it includes major systems which have been reported in previous summaries and systematic literature reviews on AES systems (Burrows et al., 2015 ; Dikli, 2011 ; Ifenthaler, 2016 ; Ifenthaler & Dikli, 2015 ; Ramesh & Sanampudi, 2021 ; Zupanc & Bosnic, 2017 ). Several AES systems also have instructional versions for classroom use. In addition to their instant scoring capacity on a holistic scale, the instructional AES systems are capable of generating diagnostic feedback and scoring on an analytic scale as well. The majority of AES systems use focus on style or content-quality and use NLP algorithms in combination with variations of regression models. Depending on the methodology, AES system requires training samples for building a reference for future comparisons. However, the test quality, precision, or accuracy of several AES systems is publicly not available or has not been reported in rigorous empirical research (Wilson & Rodrigues, 2020 ).

Open Questions and Directions for Research

There are several concerns regarding the precision of AES systems and the lack of semantic interpretation capabilities of underlying algorithms. Reliability and validity of AES systems have been extensively investigated (Landauer, Laham, & Foltz, 2003 ; Shermis et al., 2010 ). The correlations and agreement rates between AES systems and expert human raters have been found to be fairly high; however, the agreement rate is not at the desired level yet (Gierl, Latifi, Lai, Boulais, & Champlain, 2014 ). It should be noted that many of these studies highlight the results of adjacent agreement between humans and AES systems rather than those of exact agreement (Ifenthaler & Dikli, 2015 ). Exact agreement is harder to achieve as it requires two or more raters to assign the same exact score on an essay while adjacent agreement requires two or more raters to assign a score within one scale point of each other. It should also be noted that correlation studies are mostly conducted at high-stakes assessment settings rather than classroom settings; therefore, AES versus human inter-rater reliability rates may not be the same in specific assessment settings. The rate is expected to be lower in the latter since the content of an essay is likely to be more important in low-stakes assessment contexts.

The validity of AES systems has been critically reflected since the introduction of the initial applications (Page, 1966 ). A common approach for testing validity is the comparison of scores from AES systems with those of human experts (Attali & Burstein, 2006 ). Accordingly, questions arise about the role of AES systems promoting purposeful writing or authentic open-ended assessment responses, because the underlying algorithms view writing as a formulaic act and allows writers to concentrate more on the formal aspects of language such as origin, vocabulary, grammar, and text length with little or no attention to the meaning of the text (Ifenthaler, 2016 ). Validation of AES systems may include the correct use of specific assessment attributes, the openness of algorithms, and underlying aggregation and analytics techniques, as well as a combination of human and automated approaches before communicating results to learners (Attali, 2013 ). Closely related to the issue of validity is the concern regarding reliability of AES systems. In this context, reliability assumes that AES systems produce repeatedly consistent scores within and across different assessment conditions (Zupanc & Bosnic, 2015 ). Another concern is the bias of underlying algorithms, that is, algorithms have their source in a human programmer which may introduce additional error structures or even features of discrimination (e.g., cultural bias based on selective text corpora). Criticism has been put toward commercial marketing of AES systems for speakers of English as a second or foreign language (ESL/EFL) when the underlying methodology has been developed based on English language with native-English speakers in mind. In an effort to assist ESL/EFL speakers in writing classrooms, many developers have incorporated a multilingual feedback function in the instructional versions of AES systems. Receiving feedback in the first language has proven benefits, yet it may not be sufficient for ESL/EFL speakers to improve their writing in English. It would be more beneficial for non-native speakers of English if developers take common ESL/EFL errors into consideration when they build algorithms in AES systems. Another area of concern is that writers can trick AES systems. For instance, if the written text produced is long and includes certain type of vocabulary that the AES system is familiar with, an essay can receive a higher score from AES regardless of the quality of its content. Therefore, developers have been trying to prevent cheating by users through incorporating additional validity algorithms (e.g., flagging written text with unusual elements for human scoring) (Ifenthaler & Dikli, 2015 ). The validity and reliability concerns result in speculations regarding the credibility of AES systems considering that the majority of the research on AES is conducted or sponsored by the developing companies. Hence, there is a need for more research that addresses the validity and reliability issues raised above and preferably those conducted by independent researchers (Kumar & Boulanger, 2020 ).

Despite the above-mentioned concerns and limitation, educational organizations choose to incorporate instructional applications of AES systems in classrooms, mainly to increase student motivation toward writing and reducing workload of involved teachers. They assume that if AES systems assist students with the grammatical errors in their writings, teachers will have more time to focus on content related issues. Still, research on students’ perception on AES systems and the effect on motivation as well as on learning processes and learning outcomes is scarce (Stephen, Gierl, & King, 2021 ). In contrast, educational organizations are hesitant in implementing AES systems mainly because of validity issues related to domain knowledge-based evaluation. As Ramesh and Sanampudi ( 2021 ) exemplify, the domain-specific meaning of “cell” may be different in biology or physics. Other concerns that may lower the willingness to adopt of AES systems in educational organizations include fairness, consistency, transparency, privacy, security, and ethical issues (Ramineni & Williamson, 2013 ; Shermis, 2010 ).

AES systems can make the result of an assessment available instantly and may produce immediate feedback whenever the learner needs it. Such instant feedback provides autonomy to the learner during the learning process, that is, learners are not depended on possibly delayed feedback from teachers. Several attributes implemented in AES systems can produce an automated score, for instance, correctness of syntactic aspects. Still, the automated and informative feedback regarding content and semantics is limited. Alternative feedback mechanisms have been suggested, for example, Automated Knowledge Visualization and Assessment (AKOVIA) provides automated graphical feedback models, generated on the fly, which have been successfully tested for preflection and reflection in problem-based writing tasks (Lehmann, Haehnlein, & Ifenthaler, 2014 ). Other studies using AKOVIA feedback models highlight the benefits of availability of informative feedback whenever the learner needs it and its identical impact on problem solving when compared with feedback models created by domain experts (Ifenthaler, 2014 ).

Questions for future research focusing on AES systems may focus on (a) construct validity (i.e., comparing AES systems with other systems or human rater results), (b) interindividual and intraindividual consistency and robustness of AES scores obtained (e.g., in comparison with different assessment tasks), (c) correlative nature of AES scores with other pedagogical or psychological measures (e.g., interest, intelligence, prior knowledge), (d) fairness and transparency of AES systems and related scores, as well as (e) ethical concerns related to AES systems, (f) (Elliot & Williamson, 2013 ). From a technological perspective, (f) the feasibility of the automated scoring system (including training of AES using prescored, expert/reference, comparison) is still a key issue with regard to the quality of assessment results. Other requirements include the (g) instant availability, accuracy, and confidence of the automated assessment. From a pedagogical perspective, (h) the form of the open-ended or constructed-response test needs to be considered. The (i) assessment capabilities of the AES system, such as the assessment of different languages, content-oriented assessment, coherence assessment (e.g., writing style, syntax, spelling), domain-specific features assessment, and plagiarism detection, are critical for a large-scale implementation. Further, (j) the form of feedback generated by the automated scoring system might include simple scoring but also rich semantic and graphical feedback. Finally, (k) the integration of an AES system into existing applications, such as learning management systems, needs to be further investigated by developers, researchers, and practitioners.

Implications for Open, Distance, and Digital Education

The evolution of Massive Open Online Courses (MOOCs) nurtured important questions about online education and its automated assessment (Blackmon & Major, 2017 ; White, 2014 ). Education providers such as Coursera, edX, and Udacity dominantly apply so-called auto-graded assessments (e.g., single- or multiple-choice assessments). Implementing automated scoring for open-ended assessments is still on the agenda of such provides, however, not fully developed yet (Corbeil, Khan, & Corbeil, 2018 ).

With the increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings, further opportunities arise for advancing AES systems in open, distance, and digital education. Analytics-enhanced assessment enlarges standard methods of AES systems through harnessing formative as well as summative data from learners and their contexts in order to facilitate learning processes in near real-time and help decision-makers to improve learning environments. Hence, analytics-enhanced assessment may provide multiple benefits for students, schools, and involved stakeholders. However, as noted by Ellis ( 2013 ), analytics currently fail to make full use of educational data for assessment.

Interest in collecting and mining large sets of educational data on student background and performance has grown over the past years and is generally referred to as learning analytics (R. S. Baker & Siemens, 2015 ). In recent years, the incorporation of learning analytics into educational practices and research has further developed. However, while new applications and approaches have brought forth new insights, there is still a shortage of research addressing the effectiveness and consequences with regard to AES systems. Learning analytics, which refers to the use of static and dynamic data from learners and their contexts for (1) the understanding of learning and the discovery of traces of learning and (2) the support of learning processes and educational decision-making (Ifenthaler, 2015 ), offers a range of opportunities for formative and summative assessment of written text. Hence, the primary goal of learning analytics is to better meet students’ needs by offering individual learning paths, adaptive assessments and recommendations, or adaptive and just-in-time feedback (Gašević, Dawson, & Siemens, 2015 ; McLoughlin & Lee, 2010 ), ideally, tailored to learners’ motivational states, individual characteristics, and learning goals (Schumacher & Ifenthaler, 2018 ). From an assessment perspective focusing on AES systems, learning analytics for formative assessment focuses on the generation and interpretation of evidence about learner performance by teachers, learners, and/or technology to make assisted decisions about the next steps in learning and instruction (Ifenthaler, Greiff, & Gibson, 2018 ; Spector et al., 2016 ). In this context, real- or near-time data are extremely valuable because of their benefits in ongoing learning interactions. Learning analytics for written text from a summative assessment perspective is utilized to make judgments that are typically based on standards or benchmarks (Black & Wiliam, 1998 ).

In conclusion, analytics-enhanced assessments of written essays may reveal personal information and insights into an individual learning history; however, they are not accredited and far from being unbiased, comprehensive, and fully valid at this point in time. Much remains to be done to mitigate these shortcomings in a way that learners will truly benefit from AES systems.

Cross-References

Artificial Intelligence in Education and Ethics

Evolving Learner Support Systems

Introduction to Design, Delivery, and Assessment in ODDE

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Ifenthaler, D. (2023). Automated Essay Scoring Systems. In: Zawacki-Richter, O., Jung, I. (eds) Handbook of Open, Distance and Digital Education. Springer, Singapore. https://doi.org/10.1007/978-981-19-2080-6_59

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• v.10(4); 2010 Aug

A review of grading systems for evidence-based guidelines produced by medical specialties

Adrian baker, katharine young.

Clinical Standards Department, Royal College of Physicians

Jonathan Potter

Clinical Effectiveness and Evaluation Unit, Clinical Standards Department, Royal College of Physicians

Guy's and St Thomas's NHS Foundation Trust, London

The development of evidence-based guidelines requires scrupulous attention to the method of critical appraisal. Many critical appraisal systems give ‘gold standard’ status to randomised controlled trials (RCTs) due to their ability to limit bias. While guidelines with a prominent research base consisting of RCTs have been well served by such systems, specialist societies with research bases consisting of a wide range of study designs have been at a disadvantage, potentially leading to inappropriately low grades being given for recommendations. A review of the Scottish Intercollegiate Guidelines Network, the Grading of Recommendations Assessment, Development and Evaluation, the Graphic Appraisal Tool for Epidemiology and the National Service Framework for Long Term Conditions grading systems was therefore undertaken. A matrix was developed suggesting the optimum grading system for the type of guideline being developed or question being addressed by a specialist society.

Introduction

The production of an evidence-based guideline requires a systematic review and critical appraisal of the literature relevant to the scope of the guideline, as guideline recommendations are graded on the strength of evidence on which they are based. The plethora of grading systems available, make it difficult for guideline developers to choose which system to adopt resulting in different guidelines using different systems and confusion among users.

The problem that guideline developers face is that the majority of grading hierarchies are created with randomised controlled trials (RCTs) as the ‘gold standard’ due to their ability to reduce possible study biases and confounders. However, guidelines developed by specialist societies often pose questions on prognosis or patient's views, rather than on the effectiveness of pharmacological interventions; these questions are best answered by observational studies or qualitative research. This over reliance on RCTs being at the top of the evidence pinnacle often results in specialist society-based guidelines assigning inappropriately low grades to their recommendations and hence reducing their legitimacy.

The aims of this paper are to:

• review the strengths and weaknesses of the current major grading systems in the context of their use by specialist societies
• identify the optimum grading system for the type of guideline being developed or question being addressed by the specialist society.

A small working group was formed from members of the Royal College of Physicians’ Clinical Effectiveness Forum. 1 The systems that were chosen for review were the Scottish Intercollegiate Guidelines Network (SIGN), the Grading of Recommendations Assessment, Development and Evaluation (GRADE), the Graphic Appraisal Tool for Epidemiology (GATE) and the National Service Framework for Long Term Conditions (NSF-LTC) grading system. The review of SIGN was chosen due to its established use by societies; GRADE, a relatively new system, was chosen due to its perceived methodological rigour and the extensive resources used to produce its appraisal system. A review of the NSF-LTC system was conducted due to its ability to offer an alternative to SIGN and GRADE through its holistic interpretation of medical research. The GATE system was reviewed due to its simplicity, clarity and ability to be used to critically appraise different types of studies.

The review was undertaken in conjunction with discussions with the system developers and technical advisors from the National Institute for Health and Clinical Excellence (NICE), and was signed off by the Clinical Effectiveness Forum and subgroup. 2 The systems were reviewed in the context of their use by a specialist society; where members of the guideline development groups critically appraise the papers forming the evidence review of the guideline and assign grades for the evidence and recommendations. A matrix was then developed to reflect the strengths and weaknesses of each system in relation to each other and in the context of the characteristics of different fields of research.

Scottish Intercollegiate Guidelines Network

SIGN is a widely used critical appraisal and evidence hierarchy which has the advantage of being simple and clear to use and therefore suitable for small or low-resource guideline development groups. The aim of the SIGN system is to ensure that the extent of the internal and external validity of a study is robustly assessed and leads to the final grade for a recommendation. The methodology behind the system is based on a set of variables that recognise key factors, especially bias and confounding, that can influence the quality of a study or its conclusion.

The SIGN methodology includes checklists to critically appraise studies, with one checklist for each of the following study types: systematic reviews and meta-analyses, RCTs, cohort studies, case-control studies, diagnostic studies and economic studies. SIGN undertook an evaluation and iterative adaptation process for each checklist. The raison d’être of systematic appraisal is to reduce study and appraiser bias. SIGN emphasise the aspects of study design which can lead to biased results and, importantly, SIGN also acknowledges the direction of that bias. Though the methodology clearly gives the gold standard to RCTs it is recognised that non-randomised studies can strengthen or question the results of RCTs. Overall assessment of the strength of the evidence within each paper is based on a grading criteria of ‘++’, ‘+’ or ‘–’, as illustrated in Table 1 . 3

Scottish Intercollegiate Guidelines Network (SIGN) grades for evidence.

The final grade given to the evidence is based on the lowest level of evidence applicable to a key outcome produced through assessing the overall body of evidence. The reason for this is to reduce the overstatement to the risk of benefits. The grades given to the recommendations are based on an ‘A, B, C, D’ system ( Table 2 ). 3 SIGN include two caveats when grading the overall recommendation. Firstly, strong recommendations should usually not be given if they are based on only a small number of studies. Secondly, the grading level does not relate to the importance of the recommendation, but to the quality or strength of the evidence and studies that support it. 3 In essence, this means that the SIGN grading recommendation indicates the likelihood the outcome of the recommendation can be achieved.

Scottish Intercollegiate Guidelines Network (SIGN) grades for recommendations.

Grading of Recommendations Assessment, Development and Evaluation

The GRADE working group argue that confusion is created by differences and inconsistencies in existing critical appraisal systems. 4 The GRADE system was produced through extensive analysis of other systems and alternatives. The aim was to detect and resolve inherent weaknesses in the other systems while including their strengths, and producing a universal, easily understandable and practical system that can be utilised by a wide variety of practice areas in a number of different contexts. There are four levels in grading the overall quality of evidence ( Table 3 ). 5 It is contingent on the lowest quality of all outcomes that are important for making a decision.

Grading of Recommendations Assessment, Development and Evaluation (GRADE) grades for evidence.

Like SIGN, GRADE places observational studies in lower regard than RCTs, but acknowledges that there may be poor RCTs and strong observational studies. Although the study design initially leads to the hierarchical grading of a study, the study quality may raise or lower that grading. 6 RCTs with some limitations will lead to a ‘moderate’ categorisation while RCTs with a number of limitations will lead to a ‘low’ categorisation. Conversely, there may be instances where observational studies are upgraded to ‘moderate’ – or in very rare cases – ‘high quality’ categories. Diagnostic studies can be graded as high quality evidence, but to do so they generally have to be RCTs with very few limitations in study design.

The four main determinants for the strength of recommendation are:

• the balance between the desirable and undesirable consequences when compared to the alternative intervention or management strategy
• the quality of evidence and size of effect
• the value placed by stakeholders on the benefits, risks and inconvenience of the management strategy and its alternative
• high opportunity cost. 7

Quality is not the only determinant for the strength of recommendations, therefore, the GRADE system (like SIGN) separates the grading of the quality of evidence from the grading of the strength of recommendation. 8 In a move towards simplicity and clarity only two levels of recommendation are used: strong and weak. Strong recommendations mean that they should be adopted by most of the three key stakeholders (patients, physicians and policy makers). A weak recommendation, however, means that while many patients will benefit from the recommendation, clinicians and policy makers should carefully consider circumstances and contexts before abiding by it. 7

Graphic Appraisal Tool for Epidemiology

The GATE framework is largely a pictorial tool initially aimed at students and people who are not experts in epidemiology. 9 As a result, it is both simple and clear. GATE pictorially depicts the generic design for all epidemiological studies as illustrated in Fig 1 . 9 The framework consists of a triangle, circle, square and arrows, which incorporate the PECOT (or PICOT) frame (Participants, Exposure/Intervention, Comparison, Time).

The Graphic Appraisal Tool for Epidemiology (GATE) framework. (Reproduced from Annals of Internal Medicine with permission). 9

Filling in the GATE framework helps appraisers to understand what question is addressed by the study and how the investigators addressed it. This is important because on occasion the title is either obscure or asks a different question to the one answered in the study. Once the GATE frame is filled in, the study is ready to be critically appraised.

The acronym RAMMbo (Represent, Allocation/Adjustment, Maintain, Measured, Blind or objective) is used to guide assessors to ask the key questions about potential bias in a study. The GATE system and RAMMbo facilitates an assessment of the overall impact of a study's limitations. This is done by assessing the likely direction and the degree of impact each limitation has on the study. Once the overall impact of the limitations has been assessed, a judgement can be made about their impact on the study and their impact on the estimate of effect of the intervention. Utilisation of the GATE framework allows for the calculation of occurrence, incidence and size of effect.

To assist with the development of recommendations, a large ‘X’ is depicted under the GATE frame. The ‘X’ is used to identify the four quadrants of issues that need to be integrated to develop a meaningful evidence-based recommendation, including the evidence, patient values, clinical considerations and policy issues. Once the evidence is highlighted, experts are better able to consider the other factors already established by the framework to make a final recommendation. Although the GATE tool is an excellent one for teaching critical appraisal of papers, it does not assign a grade to papers or recommendations and therefore its use in guideline development is limited.

National Service Framework for Long Term Conditions grading system

The NSF-LTC typology was created to deal with the challenges of the research base of long-term conditions (LTCs). 10 Typically the research base for LTCs tends to be more varied than traditional intervention studies, and can include longitudinal, case-report and qualitative studies, as well as expert opinion. These types of studies can also be found in the literature base of other specialties, such as occupational and sexual health. Current systems do not adequately address these types of studies and are not geared towards such conditions. The characteristics of life-long conditions pose a number of obstacles that traditional RCT research designs find difficult to cope with, such as the amorphous nature of the condition and the complexity of interventions.

These characteristics in part led to the identification of three criteria required for the new NSF-LTC typology. Firstly, the viewpoint and experience of professionals, service users, their families and carers must be taken into account as valid evidence. Secondly, emphasis should be placed on the quality of the study design and its generalisability, with the acknowledgement that qualitative, quantitative and mixed studies as well as expert opinion could be equally valid depending on the context and quality of the design. Thirdly, the typology should have a framework that can be applied to all types of research design and be practical, simple and quick. 10

The type of evidence reviewed is differentiated by an ‘E’ (signifying ‘expert’ evidence – be it user, carer or professional) or ‘R’ (signifying research-based evidence) grade. Expert evidence is undertaken through consultation or consensus processes while research-based evidence is assessed in three categories (design, quality and applicability) and then awarded a grade. The design category is split into three, with each subsection containing a further subsection ( Table 4 ). 10

National Service Framework categories used to classify design.

Quality is assessed through five questions, each being scored by a 0, 1 or 2. ‘No’ is denoted by a ‘0’, ‘In part’ is denoted by a ‘1’, and ‘Yes’ is denoted by a ‘2’. The five questions are as follows:

• 1 Are the research question/aims and design clearly stated?’
• 2 Is the research design appropriate for the aims and objectives of the research?’
• 3 Are the methods clearly described?’
• 4 Is the data adequate to support the authors’ interpretations/conclusions?’
• 5 Are the results generalisable?’

A poor quality study will score three or less. A medium quality study will score between four and six, while a high quality study will score seven or above. Applicability is then rated based on ‘direct’ and ‘indirect’ subcategories and the overall rating for a research-based evidence is an amalgamation of the scores for all the categories. For example, ‘P2 high direct’ would signify that the research-based evidence is a qualitative study, which is of high quality and directly applicable to the context. Each recommendation is given a grading (A, B or C) based on the overall quality of evidence ( Table 5 ). 10

National Service Framework grades for evidence.

The final categorisation of the appropriate critical appraisal and grading system(s) as shown in Table 6 identifies the strengths and weaknesses in relation to the field of research. This is to acknowledge that the optimal system depends on the nature of the research question posed.

Summary of the strengths and weaknesses of the grading systems reviewed in relation to the type of study being appraised.

The research base of specialist societies tends to consist of a wide range of research fields and study types and to be disadvantaged by traditional grading systems. The gold standard status of RCTs within these systems means that graded recommendations in evidence-based guidelines, with a research base mostly consisting of non-RCTs, are often low. Furthermore, rigid grading systems may be misinterpreted in that users may assume that an absence of evidence means that there is evidence against a recommendation, when in reality it means that there is no evidence available for or against a clinical action. Another unintended consequence of grading systems is that those which have the highest grading of evidence of effectiveness may be given clinical priority over clinically more important recommendations which have been given a lower grading simply because they are backed by weaker evidence.

Appraisal systems have to balance simplicity with clarity while providing scope for flexibility and explicit judgements. Such a balance is difficult to maintain. The decision on which grading system should be used for specialist society guidelines depends on the research area to which the guideline questions pertain. If the research field and study designs for a guideline are largely homogenous, then one system need only be used. If, as is often the case, the study designs are heterogeneous, the specialist society will need to carefully consider the options for critical appraisal systems. While it is possible to consider using differing appraisal systems for different study designs this is likely to be confusing and impractical in reality. Specialist societies would be better advised to select the one which will most effectively address the predominant type of study design being appraised. Further work is being done to assess the ease of use and inter-assessor reliability of the grading systems reviewed in this paper.

Acknowledgements

We are grateful to Françoise Cluzeau, technical adviser, NICE; Chris Carmona, research analyst, NICE; Nichole Taske, analyst, NICE; Robin Harbour, information director, SIGN; Rod Jackson, head of epidemiology and biostatstics, School of Population Health, University of Auckland; and Kristina Pedersen for their advice on the review. We thank Barbara Smiley for her administrative assistance.

Competing interests

IM was former director of clinical standards for NHS Plus and commissioned evidence-based guidelines through the Clinical Standards Department, Royal College of Physicians. The project was funded by NHS Plus.

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