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.     

Modeling how, when, and what is learned in a simple fault-finding task

We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem-solving strategy, the relative difficulty of faults, and average fault-finding time. More important, because the model learns and transfers its learning across problems, it also accounts for the faster problem-solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition-based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. Overall, the quality of the correspondence appears to have arisen from procedural, declarative, and episodic learning all taking place within individual problem-solving episodes.     

Mental representations of affect knowledge

The circumplex structure derived from similarity ratings of affect words is assumed to be a conceptual representation of affect anchored in semantic knowledge. Recently, it been suggested that this structure is not based on semantic knowledge at all, but may instead reflect a type of episodic knowledge: The degree to which emotions covary in everyday life. In two experience-sampling studies, we compared the semantic and the episodic hypotheses by comparing participants' similarity ratings to the observed covariations in their own affective experience computed from their momentary reports. In Study 2, participants also provided estimates of the degree to which their emotions covaried. Evidence from both studies indicate that similarity judgements are related both to semantic and episodic information, indicating that a pure episodic account of similarity ratings, and the mental representation of affect that they reflect, is untenable.     

Assessing Mathematics Misunderstandings via Bayesian Inverse Planning

Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how to solve the equation and the ways in which they might answer incorrectly offers the opportunity to obtain a more nuanced perspective of their algebra skills. To automatically make sense of step-by-step solutions, we propose a Bayesian inverse planning model for equation solving that computes an assessment of a learner's skills based on her pattern of errors in individual steps and her choices about what sequence of problem-solving steps to take. Bayesian inverse planning builds on existing machine learning tools to create a generative model relating (mis)-understandings to equation solving choices. Two behavioral experiments demonstrate that the model can interpret people's equation solving and that its assessments are consistent with those of experienced teachers. A third experiment uses this model to tailor guidance for learners based on individual differences in misunderstandings, closing the loop between assessing understanding, and using that assessment within an educational technology. Finally, because the bottleneck in applying inverse planning to a new domain is in creating the model of possible student misunderstandings, we show how to combine inverse planning with an existing production rule model to make inferences about student misunderstandings of fraction arithmetic.     

Making children gesture brings out implicit knowledge and leads to learning

Speakers routinely gesture with their hands when they talk, and those gestures often convey information not found anywhere in their speech. This information is typically not consciously accessible, yet it provides an early sign that the speaker is ready to learn a particular task (S. Goldin-Meadow, 2003). In this sense, the unwitting gestures that speakers produce reveal their implicit knowledge. But what if a learner was forced to gesture? Would those elicited gestures also reveal implicit knowledge and, in so doing, enhance learning? To address these questions, the authors told children to gesture while explaining their solutions to novel math problems and examined the effect of this manipulation on the expression of implicit knowledge in gesture and on learning. The authors found that, when told to gesture, children who were unable to solve the math problems often added new and correct problem-solving strategies, expressed only in gesture, to their repertoires. The authors also found that when these children were given instruction on the math problems later, they were more likely to succeed on the problems than children told not to gesture. Telling children to gesture thus encourages them to convey previously unexpressed, implicit ideas, which, in turn, makes them receptive to instruction that leads to learning.     

The Relationship between Creativity and Interpersonal Problem-Solving Skills in Adults

The present study evaluated whether creativity training and interpersonal problem-solving training reflect equivalent or complementary skills in adults. A sample of 74 undergraduates received interpersonal problem-solving training, creativity training, neither, or both. Dependent variables included measures of problem-solving and creative performance, and problem-solving and creative style. The results suggested that creativity and interpersonal problem-solving represent complementary skills, in that each training program specifically affected performance only on related measures of performance. A combination of programs affected both abilities. Creativity training and interpersonal problem-solving training are popular psychoeducational interventions that developed in isolation from each other. Originally thought of as a mysterious process, the empirical analysis of the creative act can be traced to the work of Wallas (1926). Under the assumption that creativity is a desirable trait, a number of scales and training programs have been developed to measure and enhance creative skills. Creativity training has been used primarily in educational and industrial settings (e.g., Basadur, 1981). The principles of interpersonal problem-solving training have emerged more recently, in the work of Spivack and Shure (1974; Spivack, Platt, & Shure, 1976) and D'Zurilla (D'Zurilla & Goldfried, 1971; D'Zurilla & Nezu, 1982). These authors conceptualized interpersonal problem-solving training in the context of behavior therapy, and for this reason the literature on interpersonal problem-solving is more closely associated with therapeutic settings. Creativity and interpersonal problem-solving skills can be conceptually distinguished on the basis of their goals. Interpersonal problem-solving refers to one's skill in determining the means by which to achieve a specific end or overcome a specific problem. Creativity, on the other hand, need not be oriented towards achieving specific ends; it is associated with the capacity for thinking in new and different ways. Koestler (1964) has even argued that these two goals can be inimical, at least in adults, in that the ability to combine information in unique ways may be. hindered when the individual focuses his or her thinking on a specific problem. At the same time, there are clear similarities between the two domains of skills. Guilford (1977) noted that “creative thinking produces novel outcomes, and problem-solving involves producing a new response to a new situation, which is a novel outcome” (p. 161). Edwards and Sproull (1984) saw creativity training as a method for improving the quality of solutions to problems and increasing personal effectiveness. They considered problem-solving synonymous with creativity, since both training programs offer a variety of techniques to help identify useful solutions to problems. Similarly, Noller (1979) and others (e.g., Isaksen, Dorval, & Treffinger, 1994) have discussed the concept of creative problem solving, which attempts to integrate principles in the literature on creativity and on problem solving. Isaksen et al. conceptualized the process of creative problem solving as consisting of six steps which fall within three stages. The first stage involves understanding the problem, consisting of three steps: mess-finding, data-finding, and problem-finding. This is followed by the stage of generating ideas, involving the idea- finding step. Finally, there is planning for action, which involves solution-finding and acceptance-finding. The most important difference between the various creativity training models and the interpersonal problem-solving model lies in their emphasis. Creativity training models focus primarily on enhancing skill at generating solutions. The interpersonal problem-solving model places equal emphasis on the implementation and evaluation of potential solutions. Although many authors have suggested that participation in creativity training will have positive effects on social and interpersonal functioning (e.g., Parnes, 1987), only two studies have been conducted examining the relationship between the interpersonal problem-solving training model and creativity skills. Miller, Serafica, and Clark (1989) and Shondrick, Serafica, Clark, and Miller (1992) found that interpersonal problem-solving training for children also enhanced creativity skills, and that children's creative abilities appear to be predictive of their interpersonal problem-solving skills. The question of whether creativity and interpersonal problem-solving are equivalent, complementary, or even inimical has not been adequately addressed in the existing literature. For one thing, there are no studies examining the relationship between the two constructs in adults. This is an important question, given Koestler's (1964) conclusion that they are potentially inconsistent among adults. Second, there are no studies at all regarding the impact of creativity training on problem-solving skills in adults. The purpose of the present study was to evaluate whether creativity and interpersonal problem-solving skills can be distinguished in an adult sample.     

Beyond the purely cognitive: Belief systems,social cognitions,and metacognitions as driving forces in intellectual performance

This study explores the way that belief systems, interactions with social or experimental environments, and skills at the “control” level in decision-making shape people's behavior as they solve problems. It is argued that problem-solvers' beliefs (not necessarily consciously held) about what is useful in mathematics may determine the set of “cognitive resources” at their disposal as they do mathematics. Such beliefs may, for example, render inaccessible to them large bodies of information that are stored in long-term memory and that are easily retrieved in other circumstances. In other cases, individuals' reactions to an experimental setting (fear of failure, or the desire to “look mathematical” while being videotaped) may induce behavior that is almost pathological—and at the same time, so consistent that it can be modeled. In general, such “environmental” factors establish the context within which individuals access and utilize the information potentially at their disposal. Protocols illustrating these points are presented and discussed. A model based on an axiomatization of students' beliefs about plane geometry is outlined, and is shown to correspond closely to their problem-solving performance. A framework is offered for analyzing problem-solving performance at three qualitatively different levels: access to cognitive resources stored in LTM, executive or control decision-making, and belief systems.     

Measurement of Ability in Adaptive Learning and Assessment Systems when Learners Use On-Demand Hints

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners’ progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints.     

The development of procedural knowledge in adults engaged in a “tractor-trailer” task

The purpose of this research was to study the development of procedural knowledge in adults engaged in a novel task. Seven men and 7 women, aged 18 to 35 years, were presented a pivoting, steerable, toy tractor-trailer rig to back up, turn 90°, and park along a demarcated roadway. Their physical actions and comments were examined in detail. Results revealed three phases of development in subjects' knowledge of steering procedures and the rig's movement patterns, and in their use of feedback information. A single transformation was observed in the development of subjects' knowledge of task demands. Subjects also manifested different levels of functioning when addressing different task demands. These findings were discussed in terms of transformations in organization that shape development in the course of a problem-solving experience, and the role that across-modality representation, feedback, error, and knowledge of task demands play in such transformations. Furthermore, several differences that were observed between men and women and among individual subjects were considered.     

A model of problem solving with incomplete constraint knowledge

The problem-solving behavior of subjects presented with a series of Tower of Hanoi problems is examined. A production system model which incorporates elements of domain-specific knowledge into a general problem-solving framework is presented. Other models developed for the task are based on understanding of complete solution strategies and are not satisfactory models of nonexpert human performance. The current model discriminates between problem-solving behavior based on constraint knowledge and behavior based on nonspecific general search strategies. A variety of move choice and latency measures are used to compare the performance of the model to human subject performance.     

The link between knowledge and remembering in Alzheimer’s disease

Current research on the influence of cognitive support (e.g., activation of task-relevant prior knowledge, item organizability, retrieval cues) on episodic remembering in normal aging and Alzheimer’s disease (AD) is reviewed. Examining the effects of cognitive support on memory may shed light on the relationship between knowledge and remembering, and also provides relevant information pertaining to the development of cognitive intervention procedures. A series of studies from our own and other laboratories reveal a number of interesting empirical regularities. First, AD results in problems in utilizing cognitive support for improving memory. Conceivably, this reduction in cognitive reserve capacity is due to both the overall severity of the episodic memory impairment in AD, as well as to dementia-related deficits in the semantic network that guides encoding and retrieval of information. Nevertheless, AD patients are able to utilize cognitive support in episodic memory tasks, although they typically need more support than their healthy aged counterparts to show memory facilitation. Specifically, it is critical to provide support at both encoding and retrieval in order to demonstrate performance gains in AD. Moreover, successful utilization of retrieval support in this disease is most likely to occur when the encoding requirements force the individual to engage in elaborative cognitive activity (e.g., generation of task-relevant knowledge, categorical organization). Finally, a reduction in cognitive reserve capacity occurs later in the pathogenesis of AD than a generalized episodic memory impairment. This observation reflects the insidious nature of AD, and suggests that the transition from normal aging to AD may be continuous rather than discrete.     

Enhancing memory and imagination improves problem solving among individuals with depression

Recent work has revealed links between memory, imagination, and problem solving, and suggests that increasing access to detailed memories can lead to improved imagination and problem-solving performance. Depression is often associated with overgeneral memory and imagination, along with problem-solving deficits. In this study, we tested the hypothesis that an interview designed to elicit detailed recollections would enhance imagination and problem solving among both depressed and nondepressed participants. In a within-subjects design, participants completed a control interview or an episodic specificity induction prior to completing memory, imagination, and problem-solving tasks. Results revealed that compared to the control interview, the episodic specificity induction fostered increased detail generation in memory and imagination and more relevant steps on the problem-solving task among depressed and nondepressed participants. This study builds on previous work by demonstrating that a brief interview can enhance problem solving among individuals with depression and supports the notion that episodic memory plays a key role in problem solving. It should be noted, however, that the results of the interview are relatively short-lived.     

A Theory of Algebra-Word-Problem Comprehension and Its Implications for the Design of Learning Environments

A tutoring approach is derived from a model of problem comprehension, based on the van Dijk and Kintsch (1983; Kintsch, 1988) theory of discourse processing. A problem statement is regarded as a text from which the student must glean propositional and situational information and make critical inferences. The competent student must coordinate this information with known problem models so that formal (i.e., algebraic) operations can be applied and exact solutions can be obtained. We argue that this task is a highly reading-oriented one in which poor text comprehension and an inability to access relevant long-term knowledge lead to serious errors. In particular, poor students often omit from their solutions or misspecify necessary mathematical constraints that are based on reading inferences needed to describe fully the problem situation. Furthermore, formal algebraic expressions are so abstract that their meaning is often elusive; this contributes to mistranslations and misinterpretations. The competent approach is teachable, however. We describe experimental results with ANIMATE, a learning environment that knows nothing of the problem at hand or of the student's actions. Subjects encouraged to reason explicitly about the situations described in typical word problems consistently performed as well as or better than those who were not, in both training and transfer task. We conclude that, by using an environment that gives equations situation-based meaning through computer animation, students learn to relate formal expressions to the referent situations. This enhances problem comprehension and gives a stronger representational base to the problem-solving process. A call for evaluation methods beyond just algebra problem-solving performance is made. The implications of this work for the design of future computer-based tutors and other learning environments are also discussed.     

Effects of Knowledge Organization on Task Performance

This study proposes a knowledge organization facilitating human performance on scientifically relevant recall and problem-solving tasks. This organization is structured hierarchically so as to describe knowledge at different levels of detail; it is also task-adapted so that higher levels include information most important for implementing the intended tasks. The efficacy of this organization was assessed by two experiments, in experiment I, college-level subjects read a text and performed special training tasks to acquire knowledge of a physics topic organized either in the preceding hierarchical, or in a detailed single-level .organization; a third group read the single-level organization twice. In a subsequent test, subjects with the hierarchical organization performed appreciably better on tasks of recall, error correction, and knowledge modification. In experiment 2, subjects acquired knowledge in either of two alternative hierarchical organizations of the same physics topic, but with information distributed differently over the levels. As expected, in a subsequent test subjects performed better on those tasks depending on information from higher levels of their hierarchical organization. The specially designed training was effective in producing the desired organization of a subject's internal knowledge, but subjects with lower physics grades seemed less able to assimilate and use a hierarchical organization. Similar conclusions were obtained from a third experiment in which internal, knowledge organization was inferred from an analysis of free-recall protocols.     

The status of the concept of intelligence

Psychometric studies have shown that “general intelligence” should be broken down into the ability to apply learned solutions to new problems (crystallized intelligence) and the ability to deal with novel intellectual problems (fluid intelligence). This distinction has been amplified upon by studies of individual differences in information processing. Crystallized intelligence depends on the problem-solving schema that people have acquired and upon their efficiency in accessing information in long-term memory. Fluid intelligence is associated with the ability to access and manage relatively large amounts of information in working memory. Measures of fluid and crystallized intelligence are important predictors of objectively measured workplace performance. Studies of actual and simulated workplaces have shown that this is largely due to differences in people's ability to manage information and the speed with which the details of a job can be grasped.     

Thinking aloud during reading: A descriptive model and its application

The problem-solving aspect of reading was studied by a thinking-aloud procedure. A model was developed, describing the information attended to, the thought operation used, and the result derived. Four main operations were identified: Reading, Evoking, Interpreting , and Comparing. A coding system, based upon this model, was developed. Its interrater reliability was between 0.86 and 0.92. This system was used to study the effect of reading purpose. An experimental group was instructed to apply a text concerning creativity to the problem of inducing constructive learning at the university. A control group was assigned reading with no particular purpose in mind. The experimental group used the Interpreting operation more often. Also, the Comparing operation, applied to a comparison between the text and the subjects' own knowledge, less often resulted in disagreement. Thus, the coding system is detailed enough to reflect differences in thought processes.     

High regularities in eye-movement patterns reveal the dynamics of the visual working memory allocation mechanism

With only two to five slots of visual working memory (VWM), humans are able to quickly solve complex visual problems to near optimal solutions. To explain the paradox between tightly constrained VWM and impressively complex human visual problem-solving ability, we propose several principles for dynamic VWM allocation. In particular, we propose that complex visual information is represented in a temporal manner using only a few slots of VWM that include global and local visual chunks. We built a model of human traveling salesman problem solving based on these principles of VWM allocation and tested the model with eye-movement data. Exactly as the model predicted, human eye movements during traveling salesman problem solving have precise quantitative regularities with regard to both the general statistical pattern of attentional fixations and how they vary across individuals with different VWM capacities. Even though VWM capacity is very limited, eye movements dynamically allocate VWM resources to both local and global information, enabling attention to fine details without loss of the big picture.     

Systems Thinking for Knowledge

The capacity to engage in systems thinking is often viewed as being a product of being able to understand complex systems due to one's facility in mastering systems theories, methods, and being able to adeptly reason. Relatively little attention is paid in the systems literature to the processes of learning from experience and creating knowledge as a direct consequence of individuals engaging systems thinking itself over time. In fact, the potential efficacy of systems thinking to improve performance normally seen as only contingent on a priori knowledge, rather than knowledge created via learning from experience. Such newly create knowledge often results from engaging in modeling efforts and systemic forms of inquiry. This article proposes a model for creating new knowledge by coupling systems modeling with a pragmatic approach to knowledge-creation. This approach is based on a foundation of the pragmatic concepts first proposed by the American philosopher/scientist Charles Sanders Peirce over a century ago. This model offers systems practitioners a framework to engage in knowledge-intensive systems thinking (KIST) for addressing complex problematic issues.     

What do students remember? Episodic memory and the development of schematization

Research examining changes in memory and memory awareness during learning suggests that early in the process, students primarily have representations that are episodic in nature and experience ‘remember’ awareness during recall. However, as learning continues and schematization occurs, students' knowledge is more likely to be dominated by semantic memory representations and ‘just know’ awareness is experienced during recall. The greater the amount of remembering experienced early in learning, the more likely it is that the shift to knowing will occur in students. In this study, university students studied either material rich in distinctive features that may serve as cues to episodic memory, or material lacking in these features. Students' knowledge was tested after a 2‐day and a 5‐wk interval. In contrast to students who studied the material lacking distinctive features, students who studied the distinctively rich material showed a predominance of remember awareness on the first test, and on the follow‐up test showed a predominance of know awareness and were able to recall more details of the learning material. Copyright © 2004 John Wiley & Sons, Ltd.