Collaborative dialogue patterns in naturalistic one-to-one tutoring

Naturalistic one-to-one tutoring is more effective than traditional classroom teaching methods, but there have been few attempts to examine the features of normal tutoring that might explain its advantage. This project explored dialogue patterns in two samples of naturalistic tutoring with normal unskilled tutors (as opposed to expert tutors): graduate students tutoring undergraduates in research methods and high school students tutoring 7th graders in algebra. We analysed the extent to which those tutoring protocols manifested components that have been emphasized in contemporary pedagogical theories and intelligent tutoring systems: active student learning, sophisticated pedagogical strategies, specific examples and cases, collaborative problem solving and question answering, deep explanatory reasoning, convergance toward shared meanings, feedback, error diagnosis and remediation, and affect. The most prominent components consisted of collaborative problem solving, question answering, and explanation in the context of specific examples. We identify frequent dialogue patterns that characterize these collaborative processes.     

Pragmatics and Pedagogy: Conversational Rules and Politeness Strategies May Inhibit Effective Tutoring

In this article, we identify ways that Grice's (1975) conversational rules and P. Brown and Levinson's (1987) politeness strategies are commonly employed in one-to-one tutoring interactions. We examined two cross-aged tutoring corpora from research methods and algebra tutoring sessions to show how these rules and strategies can potentially enhance and inhibit effective tutoring. Examples of these costs and benefits are presented within a five-step dialogue frame proposed by Graesser and Person (1994). There appear to be differences in the use of these politeness strategies when algebra tutoring protocols are compared with research methods protocols. We suggest that politeness strategies are more prevalent in less constrained domains, even though their use may inhibit effective tutoring.     

Why Do Only Some Events Cause Learning During Human Tutoring?

Developers of intelligent tutoring systems would like to know what human tutors do and which activities are responsible for their success in tutoring. We address these questions by comparing episodes where tutoring does and does not cause learning. Approximately 125 hr of tutorial dialog between expert human tutors and physics students are analyzed to see what features of the dialog are associated with learning. Successful learning appears to require that the student reach an impasse. When students were not at an impasse, learning was uncommon regardless of the tutorial explanations employed. On the other hand, once students were at an impasse, tutorial explanations were sometimes associated with learning. Moreover, for different types of knowledge, different types of tutorial explanations were associated with learning different types of knowledge.     

Observing tutorial dialogues collaboratively: insights about human tutoring effectiveness from vicarious learning

The goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to four other instructional methods—one-on-one human tutoring, observing tutoring individually, collaborating without observing, and studying alone—the results showed that students learned to solve physics problems just as effectively from observing tutoring collaboratively as the tutees who were being tutored individually. We explain the effectiveness of this learning environment by postulating that such a situation encourages learners to become active and constructive observers through interactions with a peer. In essence, collaboratively observing combines the benefit of tutoring with the benefit of collaborating. The learning outcomes of the tutees and the collaborative observers, along with the tutoring dialogues, were used to further evaluate three hypotheses explaining why human tutoring is an effective learning method. Detailed analyses of the protocols at several grain sizes suggest that tutoring is effective when tutees are independently or jointly constructing knowledge: with the tutor, but not when the tutor independently conveys knowledge.     

Can Tutors Monitor Students' Understanding Accurately?

Students learn more and gain greater understanding from one-to-one tutoring. The preferred explanation has been that the tutors' pedagogical skills are responsible for the learning gains. Pedagogical skills involve skillful execution of tactics, such as giving explanations and feedback, or selecting the appropriate problems or questions to ask the students. Skillful execution of these pedagogical skills requires that they are adaptive and tailored to the individual students' understanding. To be adaptive, the tutors must be able to monitor students' understanding accurately, so that they know how and when to deliver the explanations, feedback, and questions. Before exploring whether in fact tutoring effectiveness can be attributed to tutors' pedagogical skills, we must first ascertain the accuracy with which tutors monitor their students' understanding. This article thus investigated monitoring accuracy from both the tutors' and the students' perspectives. By coding and recoding some data collected in a previous study, the article shows that tutors could only assess students' normative understanding from the perspective of the tutors' knowledge, but tutors were dismal at diagnosing the students' alternative understanding from the perspective of the students' knowledge.     

Does it really matter whether students' contributions are spoken versus typed in an intelligent tutoring system with natural language?

There is the question of whether learning differs when students speak versus type their responses when interacting with intelligent tutoring systems with natural language dialogues. Theoretical bases exist for three contrasting hypotheses. The speech facilitation hypothesis predicts that spoken input will increase learning, whereas the text facilitation hypothesis predicts typed input will be superior. The modality equivalence hypothesis claims that learning gains will be equivalent. Previous experiments that tested these hypotheses were confounded by automated speech recognition systems with substantial error rates that were detected by learners. We addressed this concern in two experiments via a Wizard of Oz procedure, where a human intercepted the learner's speech and transcribed the utterances before submitting them to the tutor. The overall pattern of the results supported the following conclusions: (1) learning gains associated with spoken and typed input were on par and quantitatively higher than a no-intervention control, (2) participants' evaluations of the session were not influenced by modality, and (3) there were no modality effects associated with differences in prior knowledge and typing proficiency. Although the results generally support the modality equivalence hypothesis, highly motivated learners reported lower cognitive load and demonstrated increased learning when typing compared with speaking. We discuss the implications of our findings for intelligent tutoring systems that can support typed and spoken input.     

Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive–affective states during learning

In an attempt to discover the facial action units for affective states that occur during complex learning, this study adopted an emote-aloud procedure in which participants were recorded as they verbalised their affective states while interacting with an intelligent tutoring system (AutoTutor). Participants’ facial expressions were coded by two expert raters using Ekman's Facial Action Coding System and analysed using association rule mining techniques. The two expert raters received an overall kappa that ranged between .76 and .84. The association rule mining analysis uncovered facial actions associated with confusion, frustration, and boredom. We discuss these rules and the prospects of enhancing AutoTutor with non-intrusive affect-sensitive capabilities.     

社会认知基本维度与主观幸福感：未来时间洞察力的中介作用

基于社会认知双视角模型和人类适应机制,探讨能动性和表达性与主观幸福感的关系及其作用机制,采用社会认知基本维度形容词词库、未来时间洞察力问卷和幸福感指数量表对全国21个省市居民进行调查,共回收有效问卷4071份,其中男性1714人（42.1%）,女性2342人（57.5%）。结果表明：（1）能动性、表达性显著正向预测主观幸福,且能动性的预测效力大于表达性;未来时间洞察力显著正向预测主观幸福感;（2）能动性、表达性能通过未来时间洞察力的中介作用间接预测个人主观幸福感。本研究结果有利于揭示能动性、表达性如何影响个体主观幸福感体验,以及能动性、表达性对主观幸福感的影响程度,对于提升个人主观幸福感有一定启示意义。     

A consideration of challenges to psychological assessment instruments used in forensic settings: Rorschach as exemplar

In this brief primer, we provide an outline of key issues that will help psychologists organize and prepare their expert testimony. These issues include the need to obtain essential sources of research, a review of the actual legal standards regarding admissibility of test data in expert testimony, the nature of the expert relative to the assessment instrument in expert testimony, the nature of legal versus scientific debate, and the examination of appropriate qualifications of expertise when offering legal testimony. In addition, we use a summary of information contained in several recent articles to address challenges directed against forensic psychological testing. We use the empirical literature on the Rorschach as an exemplar in discussing these issues, as the admissibility of the Rorschach in particular has been challenged, and the issues frequently focused on with the Rorschach are equally applicable to other psychological measures. In this article, we provide essential sources of Rorschach research regarding several empirical studies that summarize important information and directly address previous criticisms of the measure.     

Kognitive Lernermodellierung

Different types of learner models and their usefulness for tutoring have been discussed widely since the beginning of intelligent tutoring systems. In this paper we compare pragmatic and cognitive approaches of learner modeling. Pragmatic approaches consider relevant learner features for adaptive methods in learning environments and adapt different aspects of instruction to a restricted model representing these features. Cognitive approaches aim for a psychologically adequate modeling of human problem solving. We introduce the case-based learner model ELM as an example of a cognitive approach to learner modeling. The learning environments ELM-PE and ELM-ART use ELM for adaptional methods on conceptual, plan, and episodic levels and provide individual help and learning support. Especially in the case of integrated learning environments like ELM-ART which support a variety of learning activities, a combination of pragmatic and cognitive learner models is proposed to be a necessary and useful solution.     

Discovery Learning and Discovery Teaching

Sourcing, contextualization, and corroboration are document-level literacy skills that experts routinely use when working with history documents. These are also skills that educators and curricula planners expect students to acquire. We examine high school and college students' current degree of proficiency with these skills and describe our development and evaluation of a computer-based tutoring system designed to teach these skills. We observed that high school and college students who were asked to read multiple documents did not spontaneously attend to source information. Based on analyses of expert and intermediate behavior, we developed the Sourcer's Apprentice, a computer-based tutorial and practice environment for teaching students to source and corroborate while reading history texts. In 3 evaluation studies we found that students who used the Sourcer's Apprentice in place of regular classroom activity or a textbook-centered version of the same content improved at sourcing, contextualization, and corroboration on a transfer test. The Sourcer's Apprentice group also wrote essays on the topic that were more integrated, cited more sources, and referenced more information from primary and secondary sources than the comparison group.     

Expert performance in SCRABBLE: implications for the study of the structure and acquisition of complex skills

Applied psychologists have long been interested in examining expert performance in complex cognitive domains. In the present article, we report the results from a study of expert cognitive skill in which elements from two historically distinct research paradigms are incorporated -- the individual differences tradition and the expert-performance approach. Forty tournament-rated SCRABBLE players (20 elite, 20 average) and 40 unrated novice players completed a battery of domain-representative laboratory tasks and standardized verbal ability tests. The analyses revealed that elite- and average-level rated players only significantly differed from each other on tasks representative of SCRABBLE performance. Furthermore, domain-relevant practice mediated the effects of SCRABBLE tournament ratings on representative task performance, suggesting that SCRABBLE players can acquire some of the knowledge necessary for success at the highest levels of competition by engaging in activities deliberately designed to maximize adaptation to SCRABBLE-specific task constraints. We discuss the potential importance of our results in the context of continuing efforts to capture and explain superior performance across intellectual domains.     

Reasoning Through Instructional Analogies

This article aims to account for students’ assessments of the plausibility and applicability of analogical explanations, and individual differences in these assessments, by analyzing properties of students’ underlying knowledge systems. We developed a model of explanation and change in explanation focusing on knowledge elements that provide a sense of satisfaction to those judging the explanation. We call these elements “explanatory primitives.” In this model, explanations are accepted or rejected on the basis of (a) the individual's convictions concerning particular explanatory primitives and (b) the fit of these primitives to current circumstances. Data are drawn from clinical interviews with three high school students who worked through a bridging analogies tutoring sequence on the existence of the normal force in mechanics. Methodologically, our work involves fine-grain analysis of process data and explicit principles of empirical accountability; we believe it marks a methodological advance over most previously reported empirical studies of analogical reasoning.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.     

Scientific misconduct: The lessons of time

Conclusions Pascal’s paper indicates how far we have come. Now as then, however, there is a need to reflect from outside the cocoon of our agencies, institutions, and disciplines to behold the enterprise that shapes both our behavior and our interpretations of it. For the boundary separating propriety from impropriety continues to move. Just as science, and the knowledge it begets, continues to evolve, so must our collective standards. The lessons of time include this: ORI or biomedical research is no island; each is connected to a body of practitioners who are accountable to a society that is ever-more skeptical of expert knowledge and the institutions entrusted with its development. We are participants in a process of “continuous improvement”, not occupants of a state of grace. For the good of the enterprise we cherish, it is best that we all remember that. The views expressed here are the author’s own and reflect neither those of NSF nor of the National Science Board.     

Eliciting Knowledge from Experts: A Methodological Analysis

The psychological study of expertise has a rich background and has recently gained impetus in part because of the advent of expert systems and related technologies for preserving knowledge. In the study of expertise, whether in the context of applications or the context of psychological research, knowledge elicitation is a crucial step. Research in a number of traditions - judgment and decision making, human factors, cognitive science, expert systems - has utilized a variety of knowledge elicitation methods. Given the diversity of disciplines, topics, paradigms, and goals, it is difficult to make the literature cohere around a methodological theme. For discussion purposes, we place knowledge elicitation techniques into three categories: (1) analysis of the tasks that experts usually perform, (2) various types of interviews, and (3) contrived tasks which reveal an expert′s reasoning processes without necessarily asking about these processes. We illustrate types and subtypes of techniques, culminating in a discussion of research that has empirically evaluated and compared techniques. The article includes some recommendations about "how to do" knowledge elicitation, some cautionary tales, and a discussion of the prospects.     

Episodic Learner Modeling

Modeling the learner is a central aspect of intelligent tutoring systems and knowledge-based help systems that support learners in complex problem-solving domains. In this article, the episodic learner model ELM is introduced as a hybrid system that analyses novices' solutions to programming tasks based on both rule-based and case-based reasoning. ELM behaves like to a human tutor. Initially, ELM is able to analyze problem solutions based only on its domain knowledge. With increasing knowledge about a particular learner captured in a dynamic episodic case base, it adapts to the learner's individual problem-solving behavior. Two simulation studies were performed to validate the system. The first study shows that the system can learn which rules are applied successfully to diagnose code produced by programmers and that using this information reduces the computational effort of diagnoses. Using information from the episodic learner model additionally speeds up the diagnostic process. The second study shows that ELM is able to predict individual solutions. Finally, correspondences and differences to related systems are discussed.