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The PREG model of question asking assumes that questions emerge when there is cognitive disequilibrium, as in the case of contradictions, obstacles, and anomalies. Participants read illustrated texts about everyday devices (e.g., a cylinder lock) and then were placed in cognitive disequilibrium through a breakdown scenario (e.g., the key turns but the bolt does not move). The participants asked questions when given the breakdown scenario, and an eyetracker recorded their fixations. As was predicted, deep comprehenders asked better questions and fixated on device components that explained the malfunction. The eye fixations were examined before, during, and after the participants' questions in order to trace the occurrence and timing of convergence on faults, causal reasoning, and other cognitive processes. [ABSTRACT FROM AUTHOR]

This article covers recent research activities in educational psychology that have an interdisciplinary emphasis and that accommodate twenty-first-century skills in addition to the traditional foundations of literacy, numeracy, science, reasoning (problem-solving), and academic subject matter. We emphasize digital technologies because they are capable of tracking learning data in rich detail and reliably delivering interventions that are tailored to individual learners in particular sociocultural contexts. This is a departure from inflexible pedagogical approaches that previously have been routinely adopted in most classrooms and other contexts of instruction with no precise record of learning and instructional activities. A good design of educational technology embraces the principles of learning science, identifies the basic types of learning that are needed, implements relevant technological affordances, and accommodates feedback from different stakeholders. This article covers research in literacy, collaborative problem-solving, motivation, emotion, and science, technology, engineering, and mathematics (STEM) areas.

This paper describes a new automated disengagement tracking system (DTS) that detects learners' maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading comprehension skills. Learners interact with two computer agents in natural language in 30 lessons focusing on word knowledge, sentence processing, text comprehension, and digital literacy. Each lesson has one to three dozen questions to assess and enhance learning. DTS automatically retrieves and aggregates a learner's response accuracies and time on the first three to five questions in a lesson, as a baseline performance for the lesson when they are presumably engaged, and then detects disengagement by observing if the learner's following performance significantly deviates from the baseline. DTS is computed with an unsupervised learning method and thus does not rely on any self-reports of disengagement. We analyzed the response time and accuracy of 252 adult literacy learners who completed lessons in AutoTutor. Our results show that items that the detector identified as the learner being disengaged had a performance accuracy of 18.5%, in contrast to 71.8% for engaged items. Moreover, the three post-test reading comprehension scores from Woodcock Johnson III, RISE, and RAPID had a significant association with the accuracy of engaged items, but not disengaged items., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Chen, Fang, Shi, Sabatini, Greenberg, Frijters and Graesser.)

Roles are one of the most important concepts in understanding human sociocognitive behavior. During group interactions, members take on different roles within the discussion. Roles have distinct patterns of behavioral engagement (i.e., active or passive, leading or following), contribution characteristics (i.e., providing new information or echoing given material), and social orientation (i.e., individual or group). Different combinations of roles can produce characteristically different group outcomes, and thus can be either less or more productive with regard to collective goals. In online collaborative-learning environments, this can lead to better or worse learning outcomes for the individual participants. In this study, we propose and validate a novel approach for detecting emergent roles from participants' contributions and patterns of interaction. Specifically, we developed a group communication analysis (GCA) by combining automated computational linguistic techniques with analyses of the sequential interactions of online group communication. GCA was applied to three large collaborative interaction datasets (participant N = 2,429, group N = 3,598). Cluster analyses and linear mixed-effects modeling were used to assess the validity of the GCA approach and the influence of learner roles on student and group performance. The results indicated that participants' patterns of linguistic coordination and cohesion are representative of the roles that individuals play in collaborative discussions. More broadly, GCA provides a framework for researchers to explore the micro intra- and interpersonal patterns associated with participants' roles and the sociocognitive processes related to successful collaboration.

Collaborative problem solving (CPS) has been receiving increasing international attention because much of the complex work in the modern world is performed by teams. However, systematic education and training on CPS is lacking for those entering and participating in the workforce. In 2015, the Programme for International Student Assessment (PISA), a global test of educational progress, documented the low levels of proficiency in CPS. This result not only underscores a significant societal need but also presents an important opportunity for psychological scientists to develop, adopt, and implement theory and empirical research on CPS and to work with educators and policy experts to improve training in CPS. This article offers some directions for psychological science to participate in the growing attention to CPS throughout the world. First, it identifies the existing theoretical frameworks and empirical research that focus on CPS. Second, it provides examples of how recent technologies can automate analyses of CPS processes and assessments so that substantially larger data sets can be analyzed and so students can receive immediate feedback on their CPS performance. Third, it identifies some challenges, debates, and uncertainties in creating an infrastructure for research, education, and training in CPS. CPS education and assessment are expected to improve when supported by larger data sets and theoretical frameworks that are informed by psychological science. This will require interdisciplinary efforts that include expertise in psychological science, education, assessment, intelligent digital technologies, and policy.

In this study we developed and evaluated a crowdsourcing-based latent semantic analysis (LSA) approach to computerized summary scoring (CSS). LSA is a frequently used mathematical component in CSS, where LSA similarity represents the extent to which the to-be-graded target summary is similar to a model summary or a set of exemplar summaries. Researchers have proposed different formulations of the model summary in previous studies, such as pregraded summaries, expert-generated summaries, or source texts. The former two methods, however, require substantial human time, effort, and costs in order to either grade or generate summaries. Using source texts does not require human effort, but it also does not predict human summary scores well. With human summary scores as the gold standard, in this study we evaluated the crowdsourcing LSA method by comparing it with seven other LSA methods that used sets of summaries from different sources (either experts or crowdsourced) of differing quality, along with source texts. Results showed that crowdsourcing LSA predicted human summary scores as well as expert-good and crowdsourcing-good summaries, and better than the other methods. A series of analyses with different numbers of crowdsourcing summaries demonstrated that the number (from 10 to 100) did not significantly affect performance. These findings imply that crowdsourcing LSA is a promising approach to CSS, because it saves human effort in generating the model summary while still yielding comparable performance. This approach to small-scale CSS provides a practical solution for instructors in courses, and also advances research on automated assessments in which student responses are expected to semantically converge on subject matter content.

Background: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor . This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources., Results: A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research., Conclusions: The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment., Competing Interests: Not applicableThe authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We investigated the linguistic patterns in the discourse of four generations of the collective leadership of the Communist Party of China (CPC) from 1921 to 2012. The texts of Mao Zedong, Deng Xiaoping, Jiang Zemin, and Hu Jintao were analyzed using computational linguistic techniques (a Chinese formality score) to explore the persuasive linguistic features of the leaders in the contexts of power phase, the nation's education level, power duration, and age. The study was guided by the elaboration likelihood model of persuasion, which includes a central route (represented by formal discourse) versus a peripheral route (represented by informal discourse) to persuasion. The results revealed that these leaders adopted the formal, central route more when they were in power than before they came into power. The nation's education level was a significant factor in the leaders' adoption of the persuasion strategy. The leaders' formality also decreased with their increasing age and in-power times. However, the predictability of these factors for formality had subtle differences among the different types of leaders. These results enhance our understanding of the Chinese collective leadership and the role of formality in politically persuasive messages.

We examined the coherence of trauma memories in a trauma-exposed community sample of 30 adults with and 30 without posttraumatic stress disorder. The groups had similar categories of traumas and were matched on multiple factors that could affect the coherence of memories. We compared the transcribed oral trauma memories of participants with their most important and most positive memories. A comprehensive set of 28 measures of coherence including 3 ratings by the participants, 7 ratings by outside raters, and 18 computer-scored measures, provided a variety of approaches to defining and measuring coherence. A multivariate analysis of variance indicated differences in coherence among the trauma, important, and positive memories, but not between the diagnostic groups or their interaction with these memory types. Most differences were small in magnitude; in some cases, the trauma memories were more, rather than less, coherent than the control memories. Where differences existed, the results agreed with the existing literature, suggesting that factors other than the incoherence of trauma memories are most likely to be central to the maintenance of posttraumatic stress disorder and thus its treatment., ((c) 2016 APA, all rights reserved.)

Mind wandering is a phenomenon in which attention drifts away from the primary task to task-unrelated thoughts. Previous studies have used self-report methods to measure the frequency of mind wandering and its effects on task performance. Many of these studies have investigated mind wandering in simple perceptual and memory tasks, such as recognition memory, sustained attention, and choice reaction time tasks. Manipulations of task difficulty have revealed that mind wandering occurs more frequently in easy than in difficult conditions, but that it has a greater negative impact on performance in the difficult conditions. The goal of this study was to examine the relation between mind wandering and task difficulty in a high-level cognitive task, namely reading comprehension of standardized texts. We hypothesized that reading comprehension may yield a different relation between mind wandering and task difficulty than has been observed previously. Participants read easy or difficult versions of eight passages and then answered comprehension questions after reading each of the passages. Mind wandering was reported using the probe-caught method from several previous studies. In contrast to the previous results, but consistent with our hypothesis, mind wandering occurred more frequently when participants read difficult rather than easy texts. However, mind wandering had a more negative influence on comprehension for the difficult texts, which is consistent with the previous data. The results are interpreted from the perspectives of the executive-resources and control-failure theories of mind wandering, as well as with regard to situation models of text comprehension.

Student-constructed responses, such as essays, short-answer questions, and think-aloud protocols, provide a valuable opportunity to gauge student learning outcomes and comprehension strategies. However, given the challenges of grading student-constructed responses, instructors may be hesitant to use them. There have been major advances in the application of natural language processing of student-constructed responses. This literature review focuses on two dimensions that need to be considered when developing new systems. The first is type of response provided by the student-namely, meaning-making responses (e.g., think-aloud protocols, tutorial dialogue) and products of comprehension (e.g., essays, open-ended questions). The second corresponds to considerations of the type of natural language processing systems used and how they are applied to analyze the student responses. We argue that the appropriateness of the assessment protocols is, in part, constrained by the type of response and researchers should use hybrid systems that rely on multiple, convergent natural language algorithms.

The proposed multilevel framework of discourse comprehension includes the surface code, the textbase, the situation model, the genre and rhetorical structure, and the pragmatic communication level. We describe these five levels when comprehension succeeds and also when there are communication misalignments and comprehension breakdowns. A computer tool has been developed, called Coh-Metrix, that scales discourse (oral or print) on dozens of measures associated with the first four discourse levels. The measurement of these levels with an automated tool helps researchers track and better understand multilevel discourse comprehension. Two sets of analyses illustrate the utility of Coh-Metrix in discourse theory and educational practice. First, Coh-Metrix was used to measure the cohesion of the text base and situation model, as well as potential extraneous variables, in a sample of published studies that manipulated text cohesion. This analysis helped us better understand what was precisely manipulated in these studies and the implications for discourse comprehension mechanisms. Second, Coh-Metrix analyses are reported for samples of narrative and science texts in order to advance the argument that traditional text difficulty measures are limited because they fail to accommodate most of the levels of the multilevel discourse comprehension framework., (Copyright © 2010 Cognitive Science Society, Inc.)

Events have beginnings, ends, and often overlap in time. A major question is how perceivers come to parse a stream of multimodal information into meaningful units and how different event boundaries may vary event processing. This work investigates the roles of these three types of event boundaries in constructing event temporal relations. Predictions were made based on how people would err according to the beginning state, end state, and overlap heuristic hypotheses. Participants viewed animated events that include all the logical possibilities of event temporal relations, and then made temporal relation judgments. The results showed that people make use of the overlap between events and take into account the ends and beginnings, but they weight ends more than beginnings. Neural network simulations showed a self-organized distinction when learning temporal relations between events with overlap versus those without., (Copyright © 2009, Cognitive Science Society, Inc.)

We investigated the linguistic features of temporal cohesion that distinguish variations in temporal coherence. In an analysis of 150 texts, experts rated temporal coherence on three continuous scale measures designed to capture unique representations of time. Coh-Metrix, a computational tool that assesses textual cohesion, correctly predicted the human ratings with five features of temporal cohesion. The correlations between predicted and actual scores were all statistically significant. In a complementary study, we explored the importance of temporal cohesion in characterizing genre. A discriminant function analysis, using Coh-Metrix temporal indices, successfully distinguished the genres of science, history, and narrative texts. The results suggested that history texts are more similar to narrative texts than to science texts in terms of temporal cohesion.

It is often assumed that engaging in a one-on-one dialogue with a tutor is more effective than listening to a lecture or reading a text. Although earlier experiments have not always supported this hypothesis, this may be due in part to allowing the tutors to cover different content than the noninteractive instruction. In 7 experiments, we tested the interaction hypothesis under the constraint that (a) all students covered the same content during instruction, (b) the task domain was qualitative physics, (c) the instruction was in natural language as opposed to mathematical or other formal languages, and (d) the instruction conformed with a widely observed pattern in human tutoring: Graesser, Person, and Magliano's 5-step frame. In the experiments, we compared 2 kinds of human tutoring (spoken and computer mediated) with 2 kinds of natural-language-based computer tutoring (Why2-Atlas and Why2-AutoTutor) and 3 control conditions that involved studying texts. The results depended on whether the students' preparation matched the content of the instruction. When novices (students who had not taken college physics) studied content that was written for intermediates (students who had taken college physics), then tutorial dialogue was reliably more beneficial than less interactive instruction, with large effect sizes. When novices studied material written for novices or intermediates studied material written for intermediates, then tutorial dialogue was not reliably more effective than the text-based control conditions., (2007 Cognitive Science Society, Inc.)

The Human Use Regulatory Affairs Advisor (HURAA) is a Web-based facility that provides help and training on the ethical use of human subjects in research, based on documents and regulations in United States federal agencies. HURAA has a number of standard features of conventional Web facilities and computer-based training, such as hypertext, multimedia, help modules, glossaries, archives, links to other sites, and page-turning didactic instruction. HURAA also has these intelligent features: (1) an animated conversational agent that serves as a navigational guide for the Web facility, (2) lessons with case-based and explanation-based reasoning, (3) document retrieval through natural language queries, and (4) a context-sensitive Frequently Asked Questions segment, called Point & Query. This article describes the functional learning components of HURAA, specifies its computational architecture, and summarizes empirical tests of the facility on learners.

Advances in computational linguistics and discourse processing have made it possible to automate many language- and text-processing mechanisms. We have developed a computer tool called Coh-Metrix, which analyzes texts on over 200 measures of cohesion, language, and readability. Its modules use lexicons, part-of-speech classifiers, syntactic parsers, templates, corpora, latent semantic analysis, and other components that are widely used in computational linguistics. After the user enters an English text, CohMetrix returns measures requested by the user. In addition, a facility allows the user to store the results of these analyses in data files (such as Text, Excel, and SPSS). Standard text readability formulas scale texts on difficulty by relying on word length and sentence length, whereas Coh-Metrix is sensitive to cohesion relations, world knowledge, and language and discourse characteristics.

AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer. It provides the student with positive, neutral, or negative feedback on the student's typed responses, pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects erroneous ideas, answers the student's questions, and summarizes answers. AutoTutor has produced learning gains of approximately .70 sigma for deep levels of comprehension.

Qualitative methods that analyze the coherence of expository texts not only are time consuming, but also present challenges in collecting data on coding reliability. We describe software that analyzes expository texts more rapidly and produces a notable level of objectivity. ETAT (Expository Text Analysis Tool) analyzes the coherence of expository texts. ETAT adopts a symbolic representational system, known as conceptual graph structures. ETAT follows three steps: segmentation of a text into nodes, classification of the unidentified nodes, and linking the nodes with relational arcs. ETAT automatically constructs a graph in the form of nodes and their interrelationships, along with various attendant statistics and information about noninterrelated, isolated nodes. ETAT was developed in Java, so it is compatible with virtually all computer systems.

QUAID (question-understanding aid) is a software tool that assists survey methodologists, social scientists, and designers of questionnaires in improving the wording, syntax, and semantics of questions. The tool identifies potential problems that respondents might have in comprehending the meaning of questions on questionnaires. These problems can be scrutinized by researchers when they revise questions to improve question comprehension and, thereby, enhance the reliability and validity of answers. QUAID was designed to identify nine classes of problems, but only five of these problems are addressed in this article: unfamiliar technical term, vague or imprecise relative term, vague or ambiguous noun phrase, complex syntax, and working memory overload. We compared the output of QUAID with ratings of language experts who evaluated a corpus of questions on the five classes of problems. The corpus consisted of 505 questions on 11 surveys developed by the U.S. Census Bureau. Analyses of hit rates, false alarm rates, d' scores, recall scores, and precision scores revealed that QUAID was able to identify these five problems with questions, although improvements in QUAID's performance are anticipated in future research and development.

College students read chapters from a novel written by Alan Lightman (Einstein's Dreams) and later provided verification judgments on the truth/falsity of test statements. Each chapter described a different fictional village that incorporated assumptions about time that deviate from our normal TIME schema, e.g., citizens knowing exactly when the world will end, time flowing backward instead of forward. These novel assumptions about time provided interesting insights about life and reality. In two experiments, we examined whether readers could accurately incorporate these novel assumptions about time in the fictional story worlds, as manifested in the verification judgments for statements after story comprehension. The test statements included verbatim typical, verbatim atypical, inference typical, and inference atypical information from the perspective of mundane reality that meshes with a normal TIME schema. Verification ratings were collected on a 6-point scale in Experiment 1, whereas Experiment 2 used a signal-response technique in which binary true/false decisions were extracted at-.5, 1.5, 3.5, 5.5, and 10.0 s. The college students were measured on literary expertise, reading skill, working memory span, and reading time. Readers with comparatively high literary expertise showed truth discrimination scores that were compatible with a schema copy plus tag model, which assumes that readers are good at detecting and remembering atypical verbatim information; this model predicts better (and faster) truth discrimination for verbatim atypical statements than for verbatim typical statements. In contrast, fast readers with comparatively low literary expertise were compatible with a filtering model; this model predicts that readers gloss over (or suppress) atypical verbatim information and show advantages for verbatim typical information. All groups of readers had trouble inferentially propagating the novel assumptions about time in a fictional story world, but the slower readers were more accurate in their verification of the atypical inferences. A construction-integration model could explain the interactions among literary expertise, reading time, and the typicality of test statements.

The field of discourse processing has dissected many of the levels of representation that are constructed when individuals read or listen to connected discourse. These levels include the surface code, the propositional textbase, the referential situation model, the communication context, and the discourse genre. Discourse psychologists have developed models that specify how these levels are mentally represented and how they are dynamically built during comprehension. This chapter focuses on the meaning representations that are constructed when adults read written text, such as literary stories, technical expository text, and experimenter-generated "textoids." Recent psychological models have attempted to account for the identification of referents of referring expressions (e.g. which person in the text does she refer to), the connection of explicit text segments, the establishment of local and global coherence, and the encoding of knowledge-based inferences.

The authors describe a constructionist theory that accounts for the knowledge-based inferences that are constructed when readers comprehend narrative text. Readers potentially generate a rich variety of inferences when they construct a referential situation model of what the text is about. The proposed constructionist theory specifies that some, but not all, of this information is constructed under most conditions of comprehension. The distinctive assumptions of the constructionist theory embrace a principle of search (or effort) after meaning. According to this principle, readers attempt to construct a meaning representation that addresses the reader's goals, that is coherent at both local and global levels, and that explains why actions, events, and states are mentioned in the text. This study reviews empirical evidence that addresses this theory and contrasts it with alternative theoretical frameworks.

In three subject-paced experiments we evaluated reading patterns at the word, line, and sentence level for fast and slow readers. A moving-window method was used to collect word reading times for natural texts. At the word level, reading times of word N were influenced by features of word N-1 for fast readers but not for slow readers. The lag effect exhibited by fast readers indicates that they continue to process a word when it is no longer in view, thus limiting the notion of immediate processing. Contrary to our initial expectation that fast readers would process only a single new argument from a sentence, whereas slow readers would process several new arguments, we found that both reader groups adopted a many-argument strategy. However, fast and slow readers differed in terms of the text units (lines vs. sentences) defining the new-argument effects: Fast readers exhibited greater new-argument effects relative to lines, whereas slow readers exhibited greater new-argument effects relative to sentences. Specifically, slow readers integrated the new arguments primarily at the end of the sentence, whereas fast readers did so at line boundaries. These results are discussed in terms of a buffer-and-integrate model of reading comprehension.

In a subject-paced reading-time study, we examined the processing of new arguments at clause boundaries. Word reading times increased with the cumulative number of new-argument nouns at clause boundaries (as well as at sentence boundaries). New-argument nouns had a greater impact at clause boundaries than at nonboundary locations. In accordance with a buffer-integrate-purge model of reading (see Jarvella, 1979), the increase of reading times at boundaries was attributed to the integration of new information from the current sentence with prior information in the text representation. The increase at nonboundary locations was attributed to the growing load of buffering the new information. Reading times at clause boundaries were influenced to a greater extent by text-level integration than by such sentence-level processes as organization of words into clauses and linking of clauses within a sentence. The new contribution of this study was that it showed that clause boundaries provide an opportunity not only for sentence-level processes, but also for a text-level process, namely, the integration of text-new information with the growing text representation.

A holistic chunking model of sentence acquisition and retrieval is described and tested by a prompted sentence recall procedure. In this procedure, subjects first study a list of unrelated sentences and later receive single-word prompts to cue sentence recall. The model assumes that (1) words in sentences are grouped into propositions during acquisition, (2) the propositions are encoded holistically and later retrieved as units, and (3) the retrieval of one proposition does not automatically lead to recovery of other propositions in a sentence. The model was tested by patterns of intrusion errors. Noun intrusions for elements within a recovered proposition were always related conceptually to the presented nouns, even when a noun violated the co-occurrence restrictions of the verb (e.g., the tray loved the house). In contrast, noun intrusions for elements outside of the scope of a recovered proposition were often unrelated to presented nouns. It was argued that patterns of intrusion errors provide more appropriate tests for sentence structure than do quantitative patterns of correct recall, at least from the framework of the holistic chunking model.