Educational Psychology Is Evolving to Accommodate Technology, Multiple Disciplines, and Twenty-First-Century Skills.

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.

Computational Thinking Is More about Thinking than Computing.

Computational thinking is widely recognized as important, not only to those interested in computer science and mathematics but also to every student in the twenty-first century. However, the concept of computational thinking is arguably complex; the term itself can easily lead to direct connection with "computing" or "computer" in a restricted sense. In this editorial, we build on existing research about computational thinking to discuss it as a multi-faceted theoretical nature. We further present computational thinking, as a model of thinking, that is important not only in computer science and mathematics, but also in other disciplines of STEM and integrated STEM education broadly.

Automated Disengagement Tracking Within an Intelligent Tutoring System.

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.

Group communication analysis: A computational linguistics approach for detecting sociocognitive roles in multiparty interactions.

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.

Advancing the Science of Collaborative Problem Solving.

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.

Computerized summary scoring: crowdsourcing-based latent semantic analysis.

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.

Advances from the Office of Naval Research STEM Grand Challenge: expanding the boundaries of intelligent tutoring systems.

This special issue presents evaluations of four intelligent tutoring systems. These systems were funded under the Office of Naval Research's STEM Grand Challenge for intelligent tutoring systems. The systems each represent aspects of how ITS can address STEM education or how aspects of multiple systems can be integrated to support STEM education. The selected papers also provide empirical evidence for the effectiveness of each system. The current paper provides an overview of the Office of Naval Research STEM Grand Challenge program, the systems funded under the program, and summaries of the articles within this special issue.

ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics.

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.

Formality of the Chinese collective leadership.

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.

Participant, rater, and computer measures of coherence in posttraumatic stress disorder.

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.

Mind wandering while reading easy and difficult texts.

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.

Computer-based assessment of student-constructed responses.

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.

Learning, thinking, and emoting with discourse technologies.

This is an unusual moment in the history of psychology because of landmark advances in digital information technologies, computational linguistics, and other fields that use the computer to analyze language, discourse, and behavior. The technologies developed from this interdisciplinary fusion are helping students learn and think in ways that are sensitive to their cognitive and emotional states. Recent projects have developed computer technologies that help us understand the nature of conversational discourse and text comprehension in addition to improving learning. AutoTutor and other systems with conversational agents (i.e., talking heads) help students learn by holding conversations in natural language. One version of AutoTutor is sensitive to the emotions of students in addition to their cognitive states. Coh-Metrix analyzes texts on multiple levels of language and discourse, such as text genre, cohesion, syntax, and word characteristics. Coh-Metrix can assist students, teachers, principals, and policymakers when they make decisions on the right text to assign to the right student at the right time. Computers are not perfect conversation partners and comprehenders of text, but the current systems are undeniably useful. (PsycINFO Database Record (c) 2011 APA, all rights reserved).

Computational analyses of multilevel discourse comprehension.

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.

Clasificación automática de actos del habla en árabe / Automated spech act clasificati on in Arabic

Presentamos en este trabajo un método completamente automatizado para la tarea de clasificación de actos del habla en discurso árabe. La clasificación de actos del habla involucra la asignación de una categoría, obtenida a partir de una serie de categorías de actos del habla, a una oración para indicar la intención del hablante. Nuestra aproximación a la clasificación de actos del habla está basada en la hipótesis de que las palabras iniciales de una oración y/o sus partes-de-discurso son muy útiles para el diagnóstico del acto del habla expresado en la oración. Consideramos además la categorización semántica de estas palabras en términos de entidades nombradas y combinamos este enfoque con algoritmos de aprendizaje mecánicos para derivar automáticamente los parámetros de los modelos que utilizamos para implementar el enfoque. Presentamos experimentos y resultados obtenidos con varios modelos y algoritmos de aprendizaje automático en un corpus de 408 oraciones árabes.

We present in this paper a fully-automated method for the task of speech act classification for Arabic discourse. Speech act classification involves assigning a category from aset of predefined speech act categories to a sentence to indicate speaker’s intention. Our approach to speech act classification is based on the hypothesis that the initial words in a sentence and/or their parts-of-speech are very diagnostic of the particular speech act expressed in the sentence. We also consider semantic categorization ofthese words in terms of named entities and combine this approach with machinelearning algorithms to automatically derive the parameters of the models we used to implement the approach. Experiments and results obtained with several models and machine learning algorithms on a corpus of 408 Arabic sentences are presented.

Clasificación automática de actos del habla en árabe / Automated spech act clasificati on in Arabic

Presentamos en este trabajo un método completamente automatizado para la tarea de clasificación de actos del habla en discurso árabe. La clasificación de actos del habla involucra la asignación de una categoría, obtenida a partir de una serie de categorías de actos del habla, a una oración para indicar la intención del hablante. Nuestra aproximación a la clasificación de actos del habla está basada en la hipótesis de que las palabras iniciales de una oración y/o sus partes-de-discurso son muy útiles para el diagnóstico del acto del habla expresado en la oración. Consideramos además la categorización semántica de estas palabras en términos de entidades nombradas y combinamos este enfoque con algoritmos de aprendizaje mecánicos para derivar automáticamente los parámetros de los modelos que utilizamos para implementar el enfoque. Presentamos experimentos y resultados obtenidos con varios modelos y algoritmos de aprendizaje automático en un corpus de 408 oraciones árabes.(AU)

We present in this paper a fully-automated method for the task of speech act classification for Arabic discourse. Speech act classification involves assigning a category from aset of predefined speech act categories to a sentence to indicate speakers intention. Our approach to speech act classification is based on the hypothesis that the initial words in a sentence and/or their parts-of-speech are very diagnostic of the particular speech act expressed in the sentence. We also consider semantic categorization ofthese words in terms of named entities and combine this approach with machinelearning algorithms to automatically derive the parameters of the models we used to implement the approach. Experiments and results obtained with several models and machine learning algorithms on a corpus of 408 Arabic sentences are presented.(AU)

An empirical and computational investigation of perceiving and remembering event temporal relations.

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.

When are tutorial dialogues more effective than reading?

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.

Question asking and eye tracking during cognitive disequilibrium: comprehending illustrated texts on devices when the devices break down.

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.

AutoTutor: a tutor with dialogue in natural language.

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.