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Synthesis writing is widely taught across domains and serves as an important means of assessing writing ability, text comprehension, and content learning. Synthesis writing differs from other types of writing in terms of both cognitive and task demands because it requires writers to integrate information across source materials. However, little is known about how integration of source material may influence overall writing quality for synthesis tasks. This study examined approximately 900 source-based essays written in response to four different synthesis prompts which instructed writers to use information from the sources to illustrate and support their arguments and clearly indicate from which sources they were drawing (i.e., citation use). The essays were then scored by expert raters for holistic quality, argumentation, and source use/inferencing. Hand-crafted natural language processing (NLP) features and pre-existing NLP tools were used to examine semantic and keyword overlap between the essays and the source texts, plagiarism from the source texts, and instances of source citation and quoting. These variables along with text length and prompt were then used to predict essays scores. Results reported strong models for predicting human ratings that explained between 47 and 52% of the variance in scores. The results indicate that text length was the strongest predictor of score but also that more successful writers include stronger, semantically-related information from the source, provide more citations and do so later in the text, and copy less from the text. This work introduces the use of NLP techniques to assess source integration, provides details on the types of source integration used by writers, and highlights the effects of source integration on writing quality.

Literacy is a critically important and contemporary issue for educators, scientists, and politicians. Efforts to overcome the challenges associated with illiteracy, and the subsequent development of literate societies, are closely related to those of poverty reduction and sustainable human development. In this paper, the authors examine literacy from the lens of text and discourse theorists who focus on the higher-order comprehension processes involved in literacy. Discourse processing models make the assumption that comprehension emerges from the construction of a mental model of the text, which relies on the reader generating inferences to connect ideas within the text and to what the reader already knows. The article provides a broad overview of the theoretical models that drive research on text comprehension and production, as well as how this research shapes literacy instruction and effective interventions. The authors focus on two interventions with proven success in improving deep comprehension and writing, iSTART and the Writing Pal. Increasing literacy across the world call for a greater focus on theory driven strategy interventions to be integrated within classrooms and community at large.

Automated Writing Evaluation systems have been developed to help students improve their writing skills through the automated delivery of both summative and formative feedback. These systems have demonstrated strong potential in a variety of educational contexts; however, they remain limited in their personalization and scope. The purpose of the current study was to begin to address this gap by examining whether individual differences could be modeled in a source-based writing context. Undergraduate students (n=106) wrote essays in response to multiple sources and then completed an assessment of their vocabulary knowledge. Natural language processing tools were used to characterize the linguistic properties of the source-based essays at four levels: descriptive, lexical, syntax, and cohesion. Finally, machine learning models were used to predict students’ vocabulary scores from these linguistic features. The models accounted for approximately 29% of the variance in vocabulary scores, suggesting that the linguistic features of source-based essays are reflective of individual differences in vocabulary knowledge. Overall, this work suggests that automated text analyses can help to understand the role of individual differences in the writing process, which may ultimately help to improve personalization in computer-based learning environments.

This chapter focuses on the notion of the deep comprehension of text and discourse, and what it means across a broad range of diverse readers. We discuss theoretical models of comprehension and levels of comprehension, focusing in particular on the distinction between surface and deep comprehension. This distinction is discussed with respect to four groups of readers: adolescent and adult readers, younger developing readers, second language (L2) learners, and adult literacy learners. Most theories of comprehension assume that comprehension occurs at all levels of comprehension, and deeper comprehension is associated with a more coherent mental representation. Such a model leads to the conclusion that readers should be encouraged to use metacognitive reading strategies even at early stages of learning to read. However, there are implementation and translation gaps between theories of comprehension and educational practice. As a consequence, educational practice often emphasizes a linear sequence of skill development, focusing on word decoding first, and then focusing on higher order strategies only after word and sentence decoding are fully developed. Potential solutions for bridging gaps between reading researchers and to practice are discussed.

The assessment of argumentative writing generally includes analyses of the specific linguistic and rhetorical features contained in the individual essays produced by students. However, researchers have recently proposed that an individual’s ability to flexibly adapt the linguistic properties of their writing may more accurately capture their proficiency. However, the features of the task, learner, and educational context that influence this flexibility remain largely unknown. The current study extends this research by examining relations between linguistic flexibility, reading comprehension ability, and feedback in the context of an automated writing evaluation system. Students (n = 131) wrote and revised six argumentative essays in an automated writing evaluation system and were provided both summative and formative feedback on their writing. Additionally, half of the students had access to a spelling and grammar checker that provided lower-level feedback during the writing period. The results provide evidence for the supposition that skilled writers demonstrate linguistic flexibility across the argumentative essays that they produce. However, analyses also indicate that lower-level feedback (i.e., spelling and grammar feedback) have little to no impact on the properties of students’ essays nor on their variability across prompts or drafts. Overall, the current study provides important insights into the role of flexibility in argumentative writing skill and develops a strong foundation on which to conduct future research and educational interventions.

The broad use of computer-supported collaborative-learning (CSCL) environments (e.g., instant messenger-chats, forums, blogs in online communities, and massive open online courses) calls for automated tools to support tutors in the time-consuming process of analyzing collaborative conversations. In this article, the authors propose and validate the cohesion network analysis (CNA) model, housed within the ReaderBench platform. CNA, grounded in theories of cohesion, dialogism, and polyphony, is similar to social network analysis (SNA), but it also considers text content and discourse structure and, uniquely, uses automated cohesion indices to generate the underlying discourse representation. Thus, CNA enhances the power of SNA by explicitly considering semantic cohesion while modeling interactions between participants. The primary purpose of this article is to describe CNA analysis and to provide a proof of concept, by using ten chat conversations in which multiple participants debated the advantages of CSCL technologies. Each participant's contributions were human-scored on the basis of their relevance in terms of covering the central concepts of the conversation. SNA metrics, applied to the CNA sociogram, were then used to assess the quality of each member's degree of participation. The results revealed that the CNA indices were strongly correlated to the human evaluations of the conversations. Furthermore, a stepwise regression analysis indicated that the CNA indices collectively predicted 54% of the variance in the human ratings of participation. The results provide promising support for the use of automated computational assessments of collaborative participation and of individuals' degrees of active involvement in CSCL environments.

Open-ended constructed responses promote deeper processing of course materials. Further, evaluation of these explanations can yield important information about students’ cognition. This study examined how students’ constructed responses, generated at different points during learning, relate to their later comprehension outcomes. College students (N = 75) produced self-explanations during reading and explanatory retrievals after reading. The Constructed Response Assessment Tool (CRAT) was used to analyze these responses across multiple dimensions of language and relate these textual features to comprehension performance. Results indicate that the linguistic features of post-reading explanatory retrievals were more predictive of comprehension outcomes than self-explanations. Further, these models relied on different indices to predict performance.

This study examines the extent to which instructions to self-explain vs. other-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146) typed either self-explanations or other-explanations while reading a science text. The linguistic properties of these explanations were calculated using three automated text analysis tools. Machine learning classifiers in combination with the features were used to predict instruction condition (i.e., self- or other-explanation). The best machine learning model performed at rates above chance (kappa = .247; accuracy = 63%). Follow-up analyses indicated that students in the self-explanation condition generated explanations that were more cohesive and that contained words that were more related to social order (e.g., ethics). Overall, the results suggest that natural language processing techniques can be used to detect subtle differences in students' processing of complex texts.

Source-based essays are evaluated both on the quality of the writing and the content appropriate interpretation and use of source material. Hence, composing a high-quality source-based essay (an essay written based on source material) relies on skills related to both reading (the sources) and writing (the essay) skills. As such, source-based writing must involve language comprehension and production processes. The purpose of the current study is to examine the impact of reading, writing, and blended (i.e., reading and writing) strategy training on students’ performance on a content-specific source-based essay writing task. In contrast to general source-based writing tasks, content-specific source-based writing tasks are tasks wherein writers are provided the source material on which to base their essays. Undergraduate students (n = 175) were provided with strategy instruction and practice in the context of two intelligent tutoring systems, Writing Pal and Interactive Strategy Training for Active Reading and Thinking (iSTART). Results indicated that participants in the blended strategy training condition produced higher quality source-based essays than participants in the reading comprehension-only, writing-only, or control condition, with no differences observed between the latter three conditions. Further, the benefits of this blended strategy instruction remained significant regardless of prior reading and writing skills, or time on task.

This chapter outlines various methodologies that have been used to address research questions related to text memory, comprehension, and learning. It discusses innovative methods that are currently being developed to address these questions in new ways. The chapter describes various methods that have been used to assess text comprehension processes across multiple levels of representation. It provides a brief overview of research in this domain and described the most common methods for assessing text comprehension. The chapter also describes assessments that focus on the "off-line" or final understanding of a text that a reader develops, as well as assessments that tap into the processes that contribute to this understanding. The majority of contemporary models of text comprehension share a common goal of understanding the cognitive processes and knowledge that shape readers' mental representations of texts. Text comprehension is most commonly assessed using post-reading measures, which occur off-line and assess the mental representation after reading.

Self-explanations are commonly used to assess on-line reading comprehension processes. However, traditional methods of analysis ignore important temporal variations in these explanations. This study investigated how dynamical systems theory could be used to reveal linguistic patterns that are predictive of self-explanation quality. High school students (n = 232) generated self-explanations while they read a science text. Recurrence Plots were generated to show qualitative differences in students' linguistic sequences that were later quantified by indices derived by Recurrence Quantification Analysis (RQA). To predict self-explanation quality, RQA indices, along with summative measures (i.e., number of words, mean word length, and type-token ration) and general reading ability, served as predictors in a series of regression models. Regression analyses indicated that recurrence in students' self-explanations significantly predicted human rated self-explanation quality, even after controlling for summative measures of self-explanations, individual differences, and the text that was read (R2 = 0.68). These results demonstrate the utility of RQA in exposing and quantifying temporal structure in student's self-explanations. Further, they imply that dynamical systems methodology can be used to uncover important processes that occur during comprehension.

The assessment of writing proficiency generally includes analyses of the specific linguistic and rhetorical features contained in the singular essays produced by students. However, researchers have recently proposed that an individual's ability to flexibly adapt the linguistic properties of their writing might more closely capture writing skill. However, the features of the task, learner, and educational context that influence this flexibility remain largely unknown. The current study extends this research by examining relations between linguistic flexibility, reading comprehension ability, and feedback in the context of an automated writing evaluation system. Students (n = 131) wrote and revised six essays in an automated writing evaluation system and were provided both summative and formative feedback on their writing. Additionally, half of the students had access to a spelling and grammar checker that provided lower-level feedback during the writing period. The results provide evidence for the fact that developing writers demonstrate linguistic flexibility across the essays that they produce. However, analyses also indicate that lower-level feedback (i.e., spelling and grammar feedback) have little to no impact on the properties of students' essays nor on their variability across prompts or drafts. Overall, the current study provides important insights into the role of flexibility in writing skill and develops a strong foundation on which to conduct future research and educational interventions.

[Correction Notice: An Erratum for this article was reported in Vol 108(7) of Journal of Educational Psychology (see record 2016-27706-001). In the article, the author note should have included the following statement: The research reported here was supported by the Institute of Education Sciences, US Department of Education (IES R305A120707). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Institute or the US Department of Education.] A commonly held belief among educators, researchers, and students is that high-quality texts are easier to read than low-quality texts, as they contain more engaging narrative and story-like elements. Interestingly, these assumptions have typically failed to be supported by the literature on writing. Previous research suggests that higher quality writing is typically associated with decreased levels of text narrativity and readability. In this study, the authors present the hypothesis that writing proficiency is associated with an individual’s flexible use of linguistic properties, rather than simply the consistent use of a particular set of linguistic properties. To test this hypothesis, the authors leveraged both natural language processing and dynamic methodologies to capture variability in students’ use of narrative style across multiple essay prompts. Forty-five high school students wrote 16 essays across 8 laboratory sessions. Natural language processing techniques were first used to calculate the narrativity of each essay. Random walk and Euclidian distance measures were then used to visualize and classify students’ flexibility in narrativity across essays. The results support the hypotheses that students who were flexible in their use of narrativity also wrote essays that were rated as having higher quality, whereas inflexible writers tended to write lower quality essays. Additionally, more flexible writers performed higher than the more inflexible writers on general assessments of literacy and prior knowledge. These results are important for researchers and educators, as they indicate that the link between textual properties and writing quality may fluctuate according to the context of a given writing assignment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Game-based environments frequently afford students the opportunity to exert agency over their learning paths by making various choices within the environment. The combination of log data from these systems and dynamic methodologies may serve as a stealth means to assess how students behave (i.e., deterministic or random) within these learning environments. The current work captures variations in students’ behavior patterns by employing two dynamic analyses to classify students’ sequences of choices within an adaptive learning environment. Random Walk analyses and Hurst exponents were used to classify students’ interaction patterns as random or deterministic. Forty high school students interacted with the game-based system, iSTART-ME, for 11-sessions (pretest, 8 training sessions, posttest, and a delayed retention test). Analyses revealed that students who interacted in a more deterministic manner also generated higher quality self-explanations during training sessions. The results point toward the potential for dynamic analyses such as random walk analyses and Hurst exponents to provide stealth assessments of students’ learning behaviors while engaged within a game-based environment.

Using students’ process data from the game-based Intelligent Tutoring System (ITS) iSTART-ME, the current study examines students’ propensity to use system currency to unlock game-based features, (i.e., referred to here as spendency). This study examines how spendency relates to students’ interaction preferences, in-system performance, and learning outcomes (i.e., self-explanation quality, comprehension). A group of 40 high school students interacted with iSTART-ME as part of an 11-session experiment (pretest, eight training sessions, posttest, and a delayed retention test). Students’ spendency was negatively related to the frequency of their use of personalizable features. In addition, students’ spendency was negatively related to their in-system achievements, daily learning outcomes, and performance on a transfer comprehension task, even after factoring out prior ability. The findings from this study indicate that increases in students’ spendency are systematically related to their selection choices and may have a negative effect on in-system performance, immediate learning outcomes, and skill transfer outcomes. The results have particular relevance to game-based systems that incorporate currency to unlock features within games as well as to the differential tradeoffs of game features on motivation and learning.

When students exhibit control and employ a strategic plan of action over a situation they are said to be demonstrating agency (Bandura, 2001). The current work is comprised of two studies designed to investigate how agency manifests within students' choice patterns and ultimately influences self-explanation quality within the game-based system iSTART-2. In Study 1, 75 college students interacted freely within iSTART-2 for 2?h. Random walk and Entropy analyses were used to quantify the amount of control demonstrated in students' choice patterns, as well as to determine the relation between variations in these patterns and self-explanation performance within iSTART-2. Overall, students who demonstrated more controlled choice patterns generated higher quality self-explanations compared to students who exhibited more disordered choice patterns. This link between performance and controlled choice patterns is hypothesized to be driven, in part, by students' experiences of agency. That is, engaging in controlled patterns should be advantageous only when doing so is a result of students' strategic planning. In Study 2, this hypothesis was tested by assigning 70 students to a choice pattern (i.e., controlled or disordered) that had been yoked to students from Study 1, thus removing students' ability to exert agency over the iSTART-2 system. Results revealed no differences in self-explanation quality between the groups assigned to controlled and disordered choice patterns. Collectively, findings from these studies support the notion that success within game-based systems is related to students' ability to exert agency over their learning paths. Dynamical analyses revealed controlled interaction patterns.Students' feelings of agency were related to "ordered" interaction patterns.Controlled interaction patterns were related to system performance.The impact of patterns on performance was related to students' exertion of agency.

a b s t r a c t This study evaluates the use of a hierarchical classification approach to automated assessment of essays. Automated essay scoring (AES) generally relies on machine learning techniques that compute essay scores using a set of text variables. Unlike previous studies that rely on regression models, this study computes essay scores using a hierarchical approach, analogous to an incremental algorithm for hierarchical classification. The corpus in this study consists of 1243 argumentative (persuasive) essays written on 14 different prompts, across 3 different grade levels (9th grade, 11th grade, college freshman), and four different time limits for writing or temporal conditions (untimed essays and essays written in 10, 15, and 25 minute increments). The features included in the analysis are computed using the automated tools, Coh-Metrix, the Writing Assessment Tool (WAT), and Linguistic Inquiry and Word Count (LIWC). Overall, the models developed to score all the essays in the data set report 55% exact accuracy and 92% adjacent accuracy between the predicted essay scores and the human scores. The results indicate that this is a promising approach to AES that could provide more specific feedback to writers and may be relevant to other natural language computations, such as the scoring of short answers in comprehension or knowledge assessments.

In this study, we investigated the degree to which the cognitive processes in which students engage during reading comprehension could be examined through dynamical analyses of their natural language responses to texts. High school students (n = 142) generated typed self-explanations while reading a science text. They then completed a comprehension test that measured their comprehension at both surface and deep levels. The recurrent patterns of the words in students' self-explanations were first visualized in recurrence plots. These visualizations allowed us to qualitatively analyze the different self-explanation processes of skilled and less skilled readers. These recurrence plots then allowed us to calculate recurrence indices, which represented the properties of these temporal word patterns. Results of correlation and regression analyses revealed that these recurrence indices were significantly related to the students' comprehension scores at both surface- and deep levels. Additionally, when combined with summative metrics of word use, these indices were able to account for 32% of the variance in students' overall text comprehension scores. Overall, our results suggest that recurrence quantification analysis can be utilized to guide both qualitative and quantitative assessments of students' comprehension.

© 2017 ACM. There is untapped potential in achieving the full impact of learning analytics through the integration of tools into practical pedagogic contexts. To meet this potential, more work must be conducted to support educators in developing learning analytics literacy. The proposed workshop addresses this need by building capacity in the learning analytics community and developing an approach to resourcing for building 'writing analytics literacy'.

Within the educational community, research on student literacy often combines reading and writing measures, as they are presumed to draw on similar skills and background knowledge. However, relatively few studies have investigated the underlying cognitive skills required for both activities, and to what extent the required background knowledge and cognitive processes overlap. The current study investigates how individual differences commonly related to reading comprehension ability overlap and contribute to students' writing proficiency. University students ranging from 19 to 37 years old (n = 108) completed assessments to examine their reading comprehension and writing skills, as well as their vocabulary knowledge, lower-level cognitive skills (working memory), and higher-level cognitive skills (text memory, text inferencing, knowledge access, knowledge integration). Results indicated that reading comprehension was strongly related to both vocabulary knowledge and the higher-level cognitive skills. Further, writing ability was moderately associated with a subset of the measured variables, namely vocabulary knowledge and the ability to access prior knowledge. These results support the hypothesis that reading comprehension and writing share common knowledge sources and higher-level cognitive skills, although the writing process is much less reliant on these measured variables than reading comprehension.

The current study builds upon a previous study, which examined the degree to which the lexical properties of students’ essays could predict their vocabulary scores. We expand on this previous research by incorporating new natural language processing indices related to both the surface- and discourse-levels of students’ essays. Additionally, we investigate the degree to which these NLP indices can be used to account for variance in students’ reading comprehension skills. We calculated linguistic essay features using our framework, ReaderBench, which is an automated text analysis tools that calculates indices related to linguistic and rhetorical features of text. University students (n = 108) produced timed (25 minutes), argumentative essays, which were then analyzed by ReaderBench. Additionally, they completed the Gates-MacGinitie Vocabulary and Reading comprehension tests. The results of this study indicated that two indices were able to account for 32.4% of the variance in vocabulary scores and 31.6% of the variance in reading comprehension scores. Follow-up analyses revealed that these models further improved when only considering essays that contained multiple paragraph (R2 values = .61 and .49, respectively). Overall, the results of the current study suggest that natural language processing techniques can help to inform models of individual differences among student writers.

This chapter provides information about the Coh-Metrix Common Core Text Ease and Readability Assessor (TERA1), a freely available automated text analysis tool that calculates information about text difficulty at multiple levels of the text. TERA provides a profile of text difficulty encompassing five dimensions: narrativity, syntax, word concreteness, referential cohesion, and deep cohesion. TERA was developed primarily for teachers. It is specifically focused on the analysis of texts assigned to students in kindergarten through early college classes. The ability to comprehend texts is a critical skill for success in both school environments and in the workplace. As text becomes a more common medium for communication, the importance of developing strong literacy skills has subsequently increased. TERA was designed to help improve literacy skills by providing educators with the resources necessary to match student skills with appropriate texts. Many years of previous research on reading and text comprehension have been leveraged to develop the TERA dimensions.

Writing researchers have suggested that students who are perceived as strong writers (i.e., those who generate texts that are rated as high quality) demonstrate flexibility in their writing style. While anecdotally this has been a commonly held belief among researchers, scientists, and educators, there is little empirical research to support this claim. This study further investigates this hypothesis by examining how students vary in their use of two linguistic features (i.e., narrativity and cohesion) across 16 prompt-based essays. Forty-five high school students wrote 16 essays across 8 sessions within an Automated Writing Evaluation (AWE) system. Natural language processing (NLP) techniques and Entropy analyses were used to calculate how rigid or flexible students were in their use of narrative and cohesive linguistic features over time and how this trait related to individual differences in literacy abilities (i.e., vocabulary knowledge and comprehension ability), prior world knowledge, and essay quality. For instance, through the unique combination of NLP and Entropy, we found that patterns of narrative flexibility (or rigidity) was significantly and reliably related to students’ prior reading comprehension ability after 2 sessions (4 essays). Conversely, students’ flexible (or rigid) use of cohesive features was reliably related to their prior reading comprehension ability after 5 sessions (10 essays). These exploratory methodologies are important for researchers and educators, as they indicate that writing flexibility is indeed a trait of strong writers and can be detected rather quickly using the combination of textual features and dynamic analyses.

This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new approaches that measure student attributes such as demographic information, standardized test scores, and survey results. The results demonstrate that combining both text features and student attributes leads to essay scoring models that are on par with state-of-the-art scoring models. Such findings expand our knowledge of textual and non-textual features that are predictive of writing success.

The Writing Pal (W-Pal) is an intelligent tutoring system that was developed to improve students’ writing proficiency; however, it remains relatively unclear whether and how this system promotes self-regulated learning. In previous studies, we have begun to investigate the characteristics of students’ self-assessments, as well as whether interactions with W-Pal can lead to more accurate self-assessments and better writing performance. Here, we propose a series of three experiments that will test whether and how W-Pal can be used to enhance students’ self-regulated learning strategies and, consequently, influence their writing habits and the overall quality of their essays. These studies will examine the role of self-assessment in the writing process, and investigate how explicit instruction on writing criteria and self-regulated learning strategies can improve students’ writing processes and, ultimately, their writing proficiency.

We investigated linguistic factors that relate to misalignment between students’ and teachers’ ratings of essay quality. Students (n = 126) wrote essays and rated the quality of their work. Teachers then provided their own ratings of the essays. Results revealed that students who were less accurate in their self-assessments produced essays that were more causal, contained less meaningful words, and had less argument overlap between sentences.

Methods: Subjects were 102 self-identified gay, lesbian, and bisexual youth aged 18 ‐23 years. A confidential self-administered survey elicited demographic information, sexual orientation information, health care experiences, subjects’ understanding of medical confidentiality during ages 14 ‐18 years, and their suggestions for improving care to gay and lesbian adolescents. Results: Two-thirds of subjects never discussed sexual orientation with their provider but reported a desire to do so. Fewer than one-half of subjects remembered being informed about their right to medical confidentiality; those who reported being so informed were three times more likely to have discussed their sexual orientation with their provider. Over 70% of subjects who reported not being informed about their right to medical confidentiality stated that they would have been more likely to discuss sexual orientation with their provider had they been so informed. Only 13 of subjects had disclosed their sexual orientation to their health care providers. Of these, only half of the males received information on human immunodeficiency virus prevention. Conclusions: Health care providers may be failing to fully address issues of confidentiality and sexual orientation with adolescents, despite a decade of increased information on adolescent homosexuality. © Society for Adolescent Medicine, 1998

A link between alexithymia and somatization has been widely established, yet little is known about different factors that may influence this relationship. Evidence supporting the idea of psychopathology as a mediator has been presented but not widely tested, particularly in children. The present study examined depressive symptoms as a mediator of alexithymia and somatization in a sample of healthy children in order to better understand the alexithymia—somatization link from a developmental perspective. Results indicated that depression significantly partially mediated this relationship, at least for two facets of alexithymia (difficulty identifying and describing feelings). Possible mechanisms, implications, and directions for future research are discussed.

Theory and research suggest treatments targeting experiential avoidance may enhance outcomes for patients with GAD (Roemer & Orsillo, 2002; 2007). Preliminary findings demonstrate that distress about emotions and avoidance of internal experiences share unique variance with GAD above and beyond chronic reports of worry (Roemer, Salters, Raffa, & Orsillo, 2005). The purpose of the present study was to extend previous findings to explore the role of experiential avoidance and distress about emotions in a treatment-seeking sample with a principal diagnosis of GAD compared with demographically matched non-anxious controls, and to explore their shared relationship with two putative psychopathological processes in GAD: intolerance of uncertainty and worry. Patients with GAD reported significantly higher levels of experiential avoidance and distress about emotions compared to non-clinical controls while controlling for depressive symptoms, and measures of these constructs significantly predicted GAD status. Additionally, experiential avoidance and distress about anxious, positive, and angry emotions shared unique variance with intolerance of uncertainty when negative affect was partialled out, while only experiential avoidance and distress about anxious emotions shared unique variance with worry. Discussion focuses on implications for treatment as well as future directions for research.

Research has highlighted the role of emotion regulation as a common factor underlying emotional disorders. The current study examined the relationship of emotion regulation skills to obsessive-compulsive disorder (OCD) symptoms. Seven participants with a principal diagnosis of OCD in a multiple-baseline across subjects design were taught the skill of prevention of emotional avoidance in the context of emotion provocation procedures to clinically irrelevant (nonspecific) cues prior to practicing this skill with clinically relevant (OCD-specific) cues. Results suggested successful acquisition of emotion regulation skills (as evidenced by decreased thought suppression and increased acceptance of thoughts and feelings) in clinically irrelevant contexts. Acquisition of this skill was associated with decreases in obsessive-compulsive symptoms, even though clinically relevant cues were not introduced during this phase. Implementation of skills in clinically relevant contexts was associated with greater reductions in OCD symptoms. Discussion focuses on implications for emotion regulatory processes in the maintenance and treatment of emotional disorders.