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Effects of experimentally induced emotions on model-based reasoning
Abstract
Although previous research has worked with different realizations and operationalizations of positive and negative state-emotions, there is a strong agreement on the necessity of the experimental induction of emotions in order to determine their effects on cognition. Accordingly, this experimental study aims at investigating the effects of both positive and negative state-emotions on model-based reasoning processes where the emotions are experimentally induced by using a simulated feedback technique. 81 participants were randomly assigned to three experimental groups in which positive and negative state-emotions were varied during the experiment. They worked in three learning cycles where they had to solve inductive reasoning tasks. Our results indicate that participants with positive induced state-emotions outperformed participants with negative state-emotions. However, results did not reveal patterns of relation between state-emotions and the application of specific strategies. Findings are interpreted as support for the assumption of reciprocal emotions which interact with cognitive information processing.
... Many studies have shown that cognition, including multimedia learning [4,6,8,9], problem solving [10], influence [11], N-UNCOUNT [12], estimation and policy-making ability [13][14][15], cognitive flexibility [16], complex learning [17], and memory [18,19], play important roles in academic emotion in many aspects. Studies have shown that potency (from passive to active) and arousal (from quiet to excited) have different results in the cognitive process, and different effects on the relevant neural matrix [20][21][22][23]. ...
... Therefore, we believe that negative academic emotions can have various effects on learning. Positive academic moods generally have beneficial effects on the cognitive process and studying [4,6,8,11,12]. For instance, Park et al. [8] found in an eye movement study that positive academic emotions before learning can produce a much better learning effect in exams, and the retention of textual information is longer. ...
Academic emotions can have different influences on learning effects, but these have not been systematically studied. In this paper, we objectively evaluate the influence of various academic emotions on learning effects and studied the relationship between positive and negative academic emotions and learning effects by using five electronic databases, including WOS, EMBASE, PubMed, PsycINFO, and Google Scholar. According to established standards, a total of 14 articles from 506 articles were included in the analysis. We divided the 14 studies into nine intervention studies and five observational studies; five of the nine intervention studies found that students who used active learning materials performed better and had higher mental loads than those who used neutral learning materials. Positive academic emotions promoted the learning effect. Four of the five observational studies with high school, college, and postgraduate participants reported that regulating academic emotions can improve learning effects. In conclusion, this paper holds that positive academic emotions are better than negative academic emotions at improving academic performance. In future research, a new method combining multichannel video observation, physiological data, and facial expression data is proposed to capture learners’ learning behavior in various learning environments.
... Numerous studies have been investigating the neurocognitive mechanisms of word learning (Nielson and Bryant, 2005;Mestres-Missé et al., 2014;Zhao et al., 2014), but few studies considered the regulating effect of emotion on word learning, which was in stark contrast to the research trend toward exploring the interactions between cognition and emotion systems. Considerable research has investigated the role which emotion plays in numerous aspects of cognition, including memory (Brunyé et al., 2009;Baddeley, 2013), cognitive flexibility (Van Wouwe et al., 2011), problem solving (Isen et al., 1987), judgment and decision making (Isen, 1993;Schwarz, 2000;Blanchette and Richards, 2010), complex learning (D'mello and Graesser, 2012), reasoning (Ifenthaler, 2015), multitasking (Morgan and D'mello, 2016), and multimedia learning (Um and Plass, 2012;Park et al., 2014Park et al., , 2015Plass et al., 2014;Knörzer et al., 2016). However, whether emotion inhibits or facilitates successful word learning has been elusive. ...
... Words evaluated more positively appeared to be recognized faster (Larsen et al., 2008;Kousta et al., 2009;Briesemeister et al., 2011), while negative words were often found to be processed slower than neutral ones (Kuchinke et al., 2005;Larsen et al., 2008;Hofmann et al., 2009). A beneficial effect was often found for positive emotions on cognitive processes and learning (Um and Plass, 2012;Plass et al., 2014;Ifenthaler, 2015;Park et al., 2015;Morgan and D'mello, 2016). For example, an eye-tracking study by Park et al. (2015) found that positive emotional state before learning led to better learning outcomes in comprehension and transfer tests and showed longer fixation durations on the text information of the learning environment. ...
Many researchers realize that it's unrealistic to isolate language learning and processing from emotions. However, few studies on language learning have taken emotions into consideration so far, so that the probable influences of emotions on language learning are unclear. The current study thereby aimed to examine the effects of emotional states on novel word learning and their dynamic changes with learning continuing and task varying. Positive, negative or neutral pictures were employed to induce a given emotional state, and then participants learned the novel words through association with line-drawing pictures in four successive learning phases. At the end of each learning phase, participants were instructed to fulfill a semantic category judgment task (in Experiment 1) or a word-picture semantic consistency judgment task (in Experiment 2) to explore the effects of emotional states on different depths of word learning. Converging results demonstrated that negative emotional state led to worse performance compared with neutral condition; however, how positive emotional state affected learning varied with learning task. Specifically, a facilitative role of positive emotional state in semantic category learning was observed but disappeared in word specific meaning learning. Moreover, the emotional modulation on novel word learning was quite dynamic and changeable with learning continuing, and the final attainment of the learned words tended to be similar under different emotional states. The findings suggest that the impact of emotion can be offset when novel words became more and more familiar and a part of existent lexicon.
... On the one hand, several studies have found that positive-valence 3 emotions (e.g., content, enthusiastic, interested, happy, proud, and lively) improve performance. These studies propose that these emotions boost performance by promoting the activation of cognitive processes (Du et al., 2020;Ifenthaler, 2015) and engagement and interest in the task (Chauncey & Azevedo, 2010), as well as by facilitating creativity (Thompson et al., 2001;Yeh et al., 2016) and flexible thinking (Rader & Hughes, 2005). ...
Studies have shown that monetary incentives can trigger more effort but can end up hindering performance and that emotions affect performance. However, very few studies have empirically evaluated the incentive–performance relation by estimating the effect of a monetary reward on all three outcomes: effort, emotions, and performance. We estimate the effect of providing a monetary incentive on the cognitive effort, emotions, and performance of a group of university students while solving a mathematics and logical reasoning test. We evaluate the hypothesis that a monetary reward can trigger an emotional response that counteracts the positive effect of increased effort on performance. We use an eye tracker and a facial recognition algorithm to provide direct and objective measures of participants’ effort and emotional response while solving the task. On average, we find that the incentive produces more cognitive effort, induces feelings of surprise and fear, and fails to improve performance. We allow the effect of the incentive to vary according to the participant’s Grade Point Average (GPA) and find that those in the bottom 80% of the GPA distribution responded with the same additional effort as those in the top 20%. However, different from those in the top 20%, those in the bottom 80% experienced a decline in performance that coincided with a shift in anger, surprise, and fear. Overall, these results provide strong evidence consistent with our hypothesis.
... So, one of the factors that increase the achievement of the students may be their lowtest anxiety. There are many studies investigating the relationship between test anxiety and achievement and these studies show that too much test anxiety is negatively correlated with achievement (Amiri & Ghonsooly, 2015;Aşıksoy & Sorakin, 2018;Hadfield & Maddux, 1988;Horwitz, 2010;Young, 1986) Besides, positive emotions have positive effects on students' achievement compared to students with negative induced emotions (Ifenthaler, 2015). The pleasant environment may induce positive emotions of students and these positive emotions may be effective on explaining their achievement. ...
Abstract This study investigates the effect of student response system (SRS) on students’ achievement and achievement emotions in an English course. The participants were 9th-grade students (n = 83, 40 female and 43 male) attending a high school in Turkey. The study involved one experiment and two control groups. In the experimental group, SRS was used to ask questions and to provide immediate feedback to the students. Paper and pencil form of the same questions (that were used in the experimental group) were asked to the students in control group 1 and immediate feedback was provided. These questions were not used in the control group 2. Before and after the implementation, the English Achievement Test and Class Related and Test Related Achievement Emotions Questionnaires were administered to the experimental and control groups. Mixed ANOVA was used to examine the within and between-group differences. Semi-structured interviews were carried out with experimental group students. The results showed that using an SRS had a statistically significant effect on students’ achievement in English. In terms of the achievement emotions, the SRS had a statistically significant effect on decreasing students’ test anxiety. The SRS and using questions and immediate feedback in a paper and pencil format positively impacted students’ class-related boredom and enjoyment, and test relief. The results of the interviews supported these findings. Students reported that lessons with SRS were more enjoyable and SRS helped them to learn the subject matter better. They also reported that SRS helped them for the self-assessment of their progress. The findings of the study also showed that technical problems that arise from the lack of internet infrastructure might cause time management problems during the implementation of SRS.
Keywords Student response system · Achievement emotions · English course achievement · English language teaching · Secondary school students
... For example, the development of social presence through affective communication (e.g., expression of emotions, self-disclosure, use of humour) has been recognized as significant factor in group cohesion and perceived satisfaction in online learning and work (Joksimović et al., 2015;Poquet and Dawson, 2016;Schreurs et al., 2019). However, Spering and colleagues (2005) and Ifenthaler (2015) showed that emotions (i.e., positive or negative) did not significantly impact complex problem-solving performance, but the authors noted that participants with negative emotions tended to adopt different strategies in problem-solving, eliciting more systematic seeking and use of information, compared to those participants who reported positive emotions (Spering et al., 2005). However, critical to the implementation of in situ and real-time support mechanisms is the capacity to quantify diverse aspects of necessary skills and competencies. ...
Purpose
To help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al. , 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al. , 2020).
Design/methodology/approach
Complex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al. , 2006).
Findings
In this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ .
Originality/value
The commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.
... On the other hand, emotion is seen as an independent factor that is perceived and studied for its impact on advanced cognition [44], [45]. In addition, the quantitative semantic distance allows us to visually and objectively compare differences between Thinking-Entities in detail. ...
High complexity, meaning a model in which components interact in multiple ways and follow certain local rules, is a huge challenge for brain research. This paper presents a semantic vector-driven closed-loop model, namely THINKING-LOOP, for brain computing to improve the understanding and development of complex cognition. The proposed model is a three-layer fusion of data, information and knowledge with human intelligence, which exploits ontological knowledge modeling, rule-based reasoning and a human-computer interaction mechanism. The interaction and collaboration within the model depend on a pair of complementary schemes in a loop: the top-down scheme from the knowledge layer to the data layer that is used to search for stable cognitive patterns; and the bottom-up scheme from the data layer to the knowledge layer that is used to deeply analyze cognitive functions. As a key factor, human beings participate in the whole learning process of the model, which in turn assists human beings to make decisions. To verify the applicability of the present model in cognitive research, a series of fMRI experiments and analytic methods (e.g. statistical tests and network topology analysis) were conducted. The results show that the proposed model is able to take into account the characteristics of different types of brain patterns and cognitive functions, thereby achieving reasonable decision-making level.
... Since such a categorisation has used the terms emotion and affect, it is important to define the terms. An emotion refers to a mental state that arises spontaneously as opposed to arising through conscious effort (Ifenthaler, 2015). The underlying dimensions for emotion include valence, arousal, power, unpredictability and novelty (Mehu & Scherer, 2015). ...
This conceptual study develops a major theory in public relations, relationship management theory, by providing an emotions perspective on the theory, as well as expanding the theory longitudinally to the crisis context. The study first synthesised the three streams of research on emotions in public relations, revealing the need for an emotions perspective. Then, an emotions-based framework of relationship management theory is proposed by analysing (1) the emotions-based relationship cultivation strategies of positivity and negativity; (2) emotions as a mediation mechanism; (3) the emotions components of organisation-public relationships. Lastly, the theory is expanded to the crisis context, wherein the effects of relationship management and emotions are examined in times of crisis. The study theorises the invigorating effect and enervating effect at different levels of organisation-public relationships. The proposed effects as well as the emotions components and mechanisms call for further explication and empirical testing.
... In contrast, confusion and disappoint could also function as (A) design components, causing negative affect. However, the implementation of negative affect is critical as it could easily create frustration which would result in abandoning a game (Ifenthaler, 2015a). The behavioral (B) design components are related to behavioral engagement. ...
The focus of this chapter is on designing engaging educational games for cognitive, motivational, and emotional benefits. The concept of engagement is defined and its relationship with motivation and cognition are discussed. Design issues with many educational games are examined in terms of factors influencing sustained motivation and engagement. A theoretical framework to design engaging digital games is presented, including three dimensions of engagement (i.e., behavioral, cognitive, and emotional). Later, the chapter considers how to harness the appealing power of engaging games for designing engaging educational games. Various motivational features of game design and learner experiences are considered. In conclusion, the chapter also discusses various methods to assess engagement in order to inform the design of educational games that motivate learners.
... Pertinent to the current investigation, research has demonstrated that positive emotions (more than negative emotions) promote increased comprehension (Scrimin & Mason, 2015) and larger changes in attitudes (Broughton, Sinatra, & Nussbaum, 2013). In contrast, negative emotions lead to poorer integration of unexpected information (Pinheiro et al., 2013), poorer EMOTION AND REVISION 8 memory for text information (Ellis, Moore, Varner, Ottaway, & Becker, 1997;Ellis, Ottaway, Varner, Becker, & Moore, 1997;Ellis, Seibert, & Varner, 1995;Ellis, Varner, Becker, & Ottaway, 1995), and impaired inductive inferencing that is integral in the construction of situation models (Ifenthaler, 2015). Further, negative emotions are also related to derogation of information that threatens valued misconceptions (Nauroth Gollwitzer, Bender, & Rothmund, 2014) and are more likely to result in impaired learning from refutation texts (Trevors et al., 2016). ...
In recent years, a number of insights have been gained into the cognitive processes that explain how individuals overcome misconceptions and revise their previously acquired incorrect knowledge. The current study complements this line of research by investigating the moment-by-moment emotion processes that occur during knowledge revision using a think-aloud methodology. Undergraduate students read both refutation and nonrefutation texts and reported out loud their thoughts, which were coded along valence and activation dimensions of emotions. Results showed that at key points during reading, emotions differed within and between experimental text conditions. Further, exploratory mediational analysis showed that surprise was an influential emotion for learning. Findings are discussed in terms of theoretical contributions to our basic understanding of the role of emotions during knowledge revision.
... In contrast, confusion and disappoint could also function as (A) design components, causing negative affect. However, the implementation of negative affect is critical as it could easily create frustration which would result in abandoning a game (Ifenthaler, 2015a). The behavioral (B) design components are related to behavioral engagement. ...
The focus of this chapter is on designing engaging educational games for cognitive, motivational, and
emotional benefits. The concept of engagement is defined and its relationship with motivation and
cognition are discussed. Design issues with many educational games are examined in terms of factors
influencing sustained motivation and engagement. A theoretical framework to design engaging digital
games is presented, including three dimensions of engagement (i.e., behavioral, cognitive, and emotional).
Later, the chapter considers how to harness the appealing power of engaging games for designing engaging
educational games. Various motivational features of game design and learner experiences are
considered. In conclusion, the chapter also discusses various methods to assess engagement in order to
inform the design of educational games that motivate learners.
The research objective of this paper is to advance knowledge about the role of artificial intelligence (AI) in complex problem-solving. A problem is complex due to the large number of highly inter-connected variables affecting the problem state. Complex problem-solving situations often change decremental or worsen, forcing a problem solver to act immediately, under considerable time pressure. While research findings support the assumption that (1) affective, (2) (meta-)cognitive, and (3) social processes support complex problem-solving, opportunities of AI for supporting complex problem-solving need to be further investigated. This article presents a scoping review of relevant literature from the last five years. The study included, N = 38 studies for coding and analysis. Our findings show that in addition to the increased number of publications, the current trend suggests increased quality of published work. Human-AI collaboration in complex problem-solving has been explored across a broad variety of AI application domains. However, the four dimensions of complex problem – namely, cognitive, metacognitive, social and affective - have been augmented by AI to a different extent. Although most of the work has been done in the cognitive domain, it is ancouraging to see progress across social and affective dimensions as well. Implications for future research and practice are being discussed.
This study investigates the role of students' dispositional emotion reactivity in the comprehension of conflicting online information about the topic of health risks associated with the use of mobile phones. Arousal was measured by changes in electrodermal activity as a physiological response to an emotionally negative school-related video. Emotional valence was assessed by self-reports. One hundred and four 7th graders read six texts about the topic in websites varying for reliability and position. After reading, a sentence verification test assessed surface comprehension within texts, while a short essay assessed their comprehension across them at intertextual level, including sourcing and argumentation. Results revealed that two reliably distinct profiles of emotional response, high reactive and low reactive, emerged from a cluster analysis when considering both arousal and valence of emotionality. These profiles differentiated intertextual comprehension at sourcing level, while controlling for possible interfering variables. Low reactive students outperformed high reactive students for the ability to refer to source information and to connect it to the content provided. Findings indicate the importance of student differences in emotional reactivity in a common comprehension task in the digital era.
Three experiments showed that mood influences achievement attributions and that cognitive processes underlie these effects. In Experiment 1, happy Ss made more internal and stable attributions for success than failure in typical ‘life dilemmas’. In Experiment 2, attributions for real-life exam performance were more internal and stable in a happy than in a sad mood. Dysphoric moods resulted in self-critical rather than self-enhancing attributions, contrary to motivational theories, but consistent with cognitive models and the clinical literature on depression. In Experiment 3 this pattern was repeated with direct self vs. other comparisons, and for self-efficacy judgments. The results are interpreted as supporting cognitive rather than motivational theories of attribution biases. The implications of the results for clinical research, and contemporary affect–cognition theories are considered.
Mental models provide a powerful mechanism for storing knowledge in the human mind. Because of the ways these structures can influence human behavior, they have significant impact on virtually all forms of human activity. This is particularly so in the areas of instruction, learning , and expert performance. Because of the important role mental models play in these areas, this paper investigates their significance in learning a complex cognitive task in the context of troubleshooting and maintaining a complex simulated system. The focus of this investigation is on the learning-dependent progression of mental models as learners master the performance of a complex task. Accordingly, the learners´mentallearners´mental models were assessed at different points of accomplishing a complex troubleshooting skill within the realm of a simulation program. 26 engineering and chemical engineering students were recruited from engineering classes at Florida State University (14 male and 12 female). These subjects were randomly assigned to two experimental treatments (timed and self-paced). The measures of mental model progression during the second and third phase of the treatment showed that the subjects were faced with an uncertain situation which fragmented their understanding of the task. In terms of the development and progression of mental model, students ended up with the same mental model as their first one prior to receiving the treatment. The results are discussed with regard to mental model theory and instructional implications.
Research in game-based learning environments aims to recognise and show emotion. This chapter describes the main approaches and challenges involved in achieving these goals. In addition, we propose an emotional student model that can reason about students’ emotions using observable behaviour and responses to questions. Our model uses Control-Value Theory (Pekrun et al., The control value theory of achievement emotions. An integrative approach to emotions in education. In: Schutz, P.A., Pekrun, R. (eds.) Emotion in Education, pp. 13–36. Elsevier, London, 2007) as a basis for representing behaviour and was designed and evaluated using Probabilistic Relational Models (PRMs), Dynamic Bayesian Networks (DBNs) and Multinomial Logistic Regression. Olympia, a game-based learning architecture, was enhanced to incorporate affect and was used to develop PlayPhysics, an emotional game-based learning environment for teaching Physics. PlayPhysics’ design and emotional student model was evaluated with 79 students of Engineering at Tecnológico de Monterrey, Mexico City campus (ITESM-CCM). Results are presented and discussed. Future work will focus on conducting tests with a larger population of students, implementing additional game challenges and incorporating physiological signals to increase the accuracy of classification.
The assumption that positive affect leads to a better performance in simple cognitive tasks has become well established. We address the question whether positive and negative emotions differentially influence performance in complex problem-solving in the same way. Emotions were induced by positive or negative feedback in 74 participants who had to manage a computer-simulated complex problem-solving scenario. Results show that overall scenario performance is not affected, but positive and negative emotions elicit distinguishable problem-solving strategies: Participants with negative emotions are more focused on the seeking and use of information. We discuss methodological requirements for investigating emotion influences in complex and dynamic cognitive tasks.
How does negative mood affect risk taking? A brief questionnaire was used to measure state anxiety, depression, and fatigue, and a daily mood diary allowed state and trait (average level) mood to be separated. Studies 1 and 2 used natural moods and Study 3 a mood induction procedure. Risk was assessed using hypothetical everyday choice scenarios. Study 1 showed that riskiness was affected by state fatigue, but not by anxiety and depression. Study 2 showed that increased riskiness over a two - week period was predicted by fatigue changes, after controlling for riskiness and trait and state mood at time 1. Fatigue effects were stronger for more important scenarios, and when state anxiety was also high. In Study 3, covariance analyses showed that the observed increased in riskiness was related to induced fatigue, rather than to anxiety or depression. The effects are discussed in relation to the literature on fatigue effects, and models of mood and cognition.
Two experiments investigated how individual differences in attention to emotion influence the role of affect in judgments of risk. In Experiment 1, mood influenced the judgments of individuals high, but not low, in attention to emotion. When an attribution manipulation made a cause of their feelings salient, individuals high in emotional attention no longer perceived their feelings as relevant and were not influenced by them; whereas those low in emotional attention now paid attention to them and were influenced by them. This manipulation had these effects when it was presented prior to, but not in the middle of, a series of judgments. In Experiment 2, differences in response to the attribution manipulation disappeared when participants' perceptions of the relevance of their feelings were governed by instructions to use either feelings or facts as a basis for judgment. The results suggest that feelings influence judgment when they seem relevant.
This article discusses the serendipitous findings that illustrated the importance of students' and teachers' emotions during instructional interactions. Through revisiting former assumptions and findings, this article concludes that emotion is an essential part of studying motivation in classroom interactions. Emotions are intertwined in teachers' instructional responses and students' beliefs and actions, constituting an integral part of the interpersonal processes that create classroom contexts. This article calls for new theoretical syntheses and research programs that integrate emotion, motivation, and cognition as equal components in the social process of learning.
The assumption that positive affect leads to a better performance in simple cognitive tasks has become well established. We address the question whether positive and negative emotions differentially influence performance in complex problem-solving in the same way. Emotions were induced by positive or negative feedback in 74 participants who had to manage a computer-simulated complex problem-solving scenario. Results show that overall scenario performance is not affected, but positive and negative emotions elicit distinguishable problem-solving strategies: Participants with negative emotions are more focused on the seeking and use of information. We discuss methodological requirements for investigating emotion influences in complex and dynamic cognitive tasks.
reviews research on the impact of affective states on evaluative judgments, presenting evidence that is difficult to reconcile with the assumption that emotional influences on social judgment are mediated by selective recall from memory / rather, the presented research suggests that individuals frequently use their affective state at the time of judgment as a piece of information that may bear on the judgmental task, according to a "how do I feel about it" heuristic
extends the informative-functions assumption to research on affective influences on decision making and problem solving, suggesting that affective states may influence the choice of processing strategies / specifically it is argued that negative affective states, which inform the organism that its current situation is problematic, foster the use of effortful, detail oriented, analytical processing strategies, whereas positive affective states foster the use of less effortful heuristic strategies (PsycINFO Database Record (c) 2012 APA, all rights reserved)
A hierarchical facet model of self-esteem proposed by R. J. Shavelson et al (see record
1978-30429-001) was partially tested in a previous study by J. S. Fleming and W. A. Watts (see record
1981-28061-001). Their 3 factors, which were labeled Self-Regard, Social Confidence, and School Abilities, corresponded to 3 of the 4 dimensions posited by Shavelson et al. Predictions of other individual-difference variables from these factors were also tested by correlational analysis. Improvements to the instrument led to a replication of their 3 factors plus 2 predicted physical factors: Physical Appearance and Physical Abilities. In the present study, with 259 undergraduates, a 2nd-order factor analysis yielded a single, superordinate factor of global self-esteem, supporting the hierarchical interpretation of the facet model. Construct validity was further examined by replication of the correlational findings of Fleming and Watts and by correlations with other measures of personality and adjustment, including a global measure of self-esteem: the Rosenberg Self-Esteem Scale. The facet model as presently operationalized measures dimensions of relevance for the intended population, but these dimensions are not so broadly defined as to be redundant with related constructs. (65 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Aside from test anxiety scales, measurement instruments assessing students’ achievement emotions are largely lacking. This article reports on the construction, reliability, internal validity, and external validity of the Achievement Emotions Questionnaire (AEQ) which is designed to assess various achievement emotions experienced by students in academic settings. The instrument contains 24 scales measuring enjoyment, hope, pride, relief, anger, anxiety, shame, hopelessness, and boredom during class, while studying, and when taking tests and exams. Scale construction used a rational–empirical strategy based on Pekrun’s (2006) control-value theory of achievement emotions and prior exploratory research. The instrument was tested in a study using a sample of university students (N = 389). Findings indicate that the scales are reliable, internally valid as demonstrated by confirmatory factor analysis, and externally valid in terms of relationships with students’ control-value appraisals, learning, and academic performance. The results provide further support for the control-value theory and help to elucidate the structure and role of emotions in educational settings. Directions for future research and implications for educational practice are discussed.Research highlights► First comprehensive instrument measuring students’ multiple achievement emotions. ► Findings corroborate Pekrun’s control-value theory of achievement emotions. ► Need to differentiate between various discrete emotions. ► Achievement emotions linked to motivation, strategies, self-regulation, performance.
Many research studies have clearly demonstrated the importance of cognitive structures as the building blocks of meaningful
learning and retention of instructional materials. Identifying the learners’ cognitive structures will help instructors to
organize materials, identify knowledge gaps, and relate new materials to existing slots or anchors within the learners’ cognitive
structures. The purpose of our empirical investigation is to track the development of cognitive structures over time. Accordingly,
we demonstrate how various indicators derived from graph theory can be used for a precise description and analysis of cognitive
structures. Our results revealed several patterns that helped us to better understand the construction and development of
cognitive structures over time. We conclude by identifying applications of our approach for learning and instruction and proposing
possibilities for the further development of our approach.
KeywordsCognitive structure–Mental model–Concept map–Hierarchical linear model
This chapter explores the mechanisms underlying motivation and self-regulation from a functional-design perspective. Traditional approaches emphasize the mediating role of beliefs and other cognitive contents. An example of this approach is classical expectancy–value theory according to which a student's motivation to invest time and effort depends on his or her expectation of success and on the perceived value of good achievement. Learned helplessness is a practical example that illustrates the difference between content-based and functional explanations: After exposure to uncontrollable failure, many people lose their motivation and show impaired performance just as depressed patients do in response to adverse life conditions. According to traditional theorizing, those motivational and cognitive deficits are attributable to negative beliefs, such as pessimistic beliefs about one's own abilities. In contrast, according to a functional account, pessimistic beliefs and motivational deficits are consequences rather than causes of performance deficits that occur when people are confronted with uncontrollable failure: Experimental evidence shows that generalized pessimistic control beliefs typically occur after, not before, people develop symptoms of helplessness and depression. According to these findings, learned helplessness and depression cannot be remedied through making people believe in their abilities as attempted in cognitive therapy until one has established the necessary abilities. Specifying the mechanisms that underlie self-regulatory abilities is the target of functional approaches to self-regulation.
Theories in psychology make implicit or explicit assumptions about the way people store and use information. The choice of a format for knowledge representation is crucial, because it influences what processes are easy or hard for a system to accomplish. In this chapter, I define the concept of a representation. Then, I review three broad types of representations that have been incorporated into many theories. Finally, I examine proposals for the role of body states in representation as well as proposals that the concept of knowledge representation has outlived its usefulness.
Attention deployment and generating specific types of cognitions are central cognitive mechanisms of emotion regulation. Two groups of hypotheses make contradicting predictions about the emotion-cognition relationship. The moodcongruency hypothesis expects the emergence of mood-congruent cognitions (i.e., negative mood leads to negative and positive mood to positive cognitions). Similarly, a substantial body of research suggests that negative mood induces selffocus, whereas positive mood elicits an external focus of attention. The moodrepair hypothesis, on the other hand, assumes that persons in a negative mood state summon thoughts incongruent with that state and divert attention away from the self. However, the temporal sequence of cognitions assessed as well as coping dispositions, such as vigilance and cognitive avoidance, may moderate these relationships. Positive and negative emotional states were elicited by exposing the participants to the experience of success or failure in a demanding cognitive task. Cognitions that were present after emotion induction were assessed by means of a thought-listing procedure. For the total sample, results clearly confirmed the moodcongruency hypothesis. Thought order was a critical factor only for changes in self-focus. Thought valence (positive, neutral, negative) as well as self-focus were substantially influenced by coping dispositions.
According to self-determination theory, the satisfaction of the basic needs for autonomy, competence, and relatedness influences achievement emotions and situational interest. The present study investigated whether domain-specific explicit need strength moderated the impact of need satisfaction/dissatisfaction on the outcomes achievement emotions and situational interest. Self-report measures of domain specific need strength, perceived need satisfaction/dissatisfaction, achievement emotions (joy and boredom), and situational interest (catch-SI and hold-SI) were completed by 220 students attending 8th and 9th grade. Explicit need strength moderated the impact of perceived need satisfaction on hold-SI. Additionally, need strength moderated the impact of perceived need dissatisfaction on joy, boredom, and hold-SI. Nevertheless, need satisfaction had greater predictive power than need strength and the interaction effects appeared more consistently with the predictor perceived need dissatisfaction. Thus, need satisfaction seems to be the main explanatory variable for the outcomes. Conclusions are discussed.
In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper contextual and social support. More specifically, we first will identify some key elements of the structure and function of mental models in contrast to schemas. Next, these key elements of modeling will be used to generate some conjectures about the foundations of model-based reasoning. In the next section, we will describe the learning-dependent progression of mental models as a suitable approach for understanding the basics of deductive and inductive reasoning based on models as “tools for thought.” The rationale of mental models as tools for reasoning will be supported by empirical research to be described in a particular section of this paper. Finally, we will turn to the instructional implications of model-based reasoning by discussing appropriate instructional methods to affect the construction of mental models for performing deductive and inductive reasoning.
Cognitive scientists have studied internal cognitive structures, processes, and systems for decades in order to understand how they function in human learning. Nevertheless, questions concerning the diagnosis of changes in these cognitive structures while solving inductive reasoning tasks are still being scrutinized. This paper reports findings from an experimental study in which 64 participants in three experimental groups solved tasks at ten measurement points. We were able to illuminate changes of cognitive structures and found significant differences between the treatments. The results also indicate that supportive information is an important aid for developing cognitive structures while solving inductive reasoning tasks.
An abstract is not available.
Recent research has refuted the behaviorist approach by establishing a relationship between emotion and behavior. The data collection procedure, however, has often involved an inferred emotional state from a hypothetical situation. As partial fulfillment of a class requirement, 60 college students were asked to perform two problem solving tasks from the Wechsler Adult Intelligence Scale-Revised. Ten emotions were assessed by self-report on a 9-point scale. Subjects were asked before the tasks how well they though they would do and how much they would like it. Following the tasks the students were again asked in retrospect how well they thought they did and how much they liked the task. To assess the changes, pre-task scores were subtracted from the post-task scores. The results revealed that changes in emotional state from before to after the task were related to the subject's motivation toward the task. Altering the emotions of the subjects while engaged in problem solving altered the relationship between emotion and motivation. It was also found that motivation toward one type of problem solving could predict motivation toward other similar types. The self-report scales of emotions greatly enhanced the ability to predict. This relationship between motivation and emotional state can have an impact of the teaching of problem solving to children. (TW)
This paper describes a series of empirical studies, over a period of three years, designed to investigate the nature of learners’ difficulties with the proof technique of structural induction. The learners concerned were second‐year university students who had all completed a course in discrete mathematics and were currently attending a first course in Formal Methods of Computer Science. The main findings are applicable to both mathematical and structural induction, and can be ascribed to conceptual gaps. The precise nature of the errors made varied with the task and the teaching context, but a constant factor across all the studies was the failure of the majority of the subjects to appreciate the role of implication in induction. We suggest that this may be due, in part, to the conceptual gap between the main goal of the proof and the subgoals; the main goal might give false cues as to the structure of the subgoals. Another difficulty seen concerned the conceptual gap between knowing how to apply the principle of induction and understanding why it is valid. Our data indicate that performance may be improved by shifting teaching emphasis and by drawing learners’ attention to the structure of a recursive definition.
examines the major proposals for representing complex knowledge domains and attempts to show that many of the crucial theoretical differences can be captured by an analysis in terms of . . . a distinction between underlying knowledge structures and the episodic representations formed from these underlying structures and . . . a distinction between representations which are derived from old generic knowledge and representations which are constructed at the time of use / directions for future research / situational models (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Reflecting the full range of work being done across disciplines, this [handbook] reports on an ever-growing, important body of research [on emotions]. [It] is a basic resource for everything that is known about emotions.
A broad interdisciplinary overview demonstrates the vast territory affected by scholarship in the field.
Chapters address the models and research emanating from clinical and social psychology, development, biology, neurophysiology, behavior genetics, sociology, history, anthropology, and philosophy. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
lately, a number of investigators in cognitive psychology and artificial intelligence have proposed that some puzzling facts about thinking can be explained through what they call "mental models"
people's ability to understand connected discourse, their knowledge of science, and their skill in reasoning are all topics that mental models are supposed to elucidate
the point that I hope will emerge is that the advantages of mental models can all be obtained without commitment to literal models-in-the-head, and that the figurative position is therefore the more reasonable one (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This article reviews evidence indicating that, in most circumstances, positive affect enhances problem solving and decision making, leading to cognitive processing that is not only flexible, innovative, and creative, but also thorough and efficient. These results have implications regarding consumers’ potential reactions to affect generated by ads, products, consumption situations, and service encounters.
These cognitive effects of positive affect are considered in the context of effects on social interaction that show that positive affect leads to helping, generosity, and interpersonal understanding. Together, these findings suggest implications especially for customer satisfaction, and particularly for a role for employee positive affect, or employee satisfaction, in generating customer satisfaction.
Moreover, studies specifically in the domain of medical decision making and problem solving indicate that these implications would apply to the specific areas of doctor–patient interaction, medical decision making, and medical consumer satisfaction. Finally, it is suggested that the benefits of positive affect be considered when formulating healthcare policy and be included in economic models and policy decisions more generally as well.
Excerpts available on Google Books.
The emerging field of emotion regulation studies how individuals influence which emotions they have, when they have them, and how they experience and express them. This review takes an evolutionary perspective and characterizes emotion in terms of response tendencies. Emotion regulation is defined and distinguished from coping, mood regulation, defense, and affect regulation. In the increasingly specialized discipline of psychology, the field of emotion regulation cuts across traditional boundaries and provides common ground. According to a process model of emotion regulation, emotion may be regulated at five points in the emotion generative process: (a) selection of the situation, (b) modification of the situation, (c) deployment of attention, (d) change of cognitions, and (e) modulation of responses. The field of emotion regulation promises new insights into age-old questions about how people manage their emotions. (PsycINFO Database Record (c) 2013 APA, all rights reserved)
Our everyday experiences leave little doubt that our emotions can influence the decisions we make, much as the outcome of our decisions can influence the emotions we experience. Yet, the complex interplay of emotion, cognition, and decision making has received limited systematic attention in empirical research. The diverse contributions to this Special Issue address different aspects of this interplay. To place these contributions into context, this paper provides a short and selective discussion of the multiple links between emotion, cognition, and decision making. I first address the influence of moods and emotions experienced at the time of decision making. Subsequently, I turn to the affective consequences of decisions and the role of anticipated and remembered affect in decision making.
The effectiveness and validity of 11 important mood induction procedures (MIPs) were comparatively evaluated by meta-analytical procedures. Two hundred and fifty effects of the experimental induction of positive, elated and negative, depressed mood in adult, non-clinical samples were integrated. Effect sizes were generally larger for negative than for positive mood inductions. The presentation of a film or story turned out to be most effective in inducing both positive and negative mood states. The effects are especially large when subjects are explicitly instructed to enter the specified mood state. For elated mood, all other MIPs yielded considerably lower effectiveness scores. For the induction of negative mood states, Imagination, Velten, Music, Social Interaction and Feedback MIPs were about as effective as the Film/Story MIP without instruction. Induction effects covaried with several study characteristics. Effects tend to be smaller when demand characteristics are controlled or subjects are not informed about the purpose of the experiment. For behavioural measures, effects are smaller than for self-reports but still larger than zero. Hence, the effects of MIPs can be partly, but not fully due to demand effects.
System dynamics is often brought into connection with a double-loop learning process. Learning has been the object of an increasing number of studies. However, inquiry has focused on using models rather than modeling, and there are huge differences in assessment approaches. If learning changes models then it can be inferred from comparing models. Here it is argued that monitoring learning from modeling is feasible and desirable. One possibility is to conceive of a model as a series of versions and compare their structure. One possible method for comparing model versions is presented. Copyright © 2006 John Wiley & Sons, Ltd.
Although the cognitive processing of personally relevant feedback appears to be an important clinical issue, previous research has generally not examined this process. The present study presented a theoretical framework for conceptualizing the cognitive processing of feedback and employed this framework to investigate the influence of mood and feedback favorability upon feedback processing. Generally, it was proposed that in order for feedback to be understood and assimilated it must be processed deeply, where depth refers to the amont of cognitive analysis that the feedback receives. Using both reaction time and feedback recall to examine depth of processing, results indicated that unfavorable feedback is processed deeply when individuals are primed by a prior negative mood experience. Prior priming by a positive mood experience did not appear to have similar processing effects upon favorable feedback. Implications of these results for psychotherapy were discussed.
This article presents an aspect of systematicinstructional design which has received relativelylittle attention so far: strategies for makinginstruction more emotionally sound. The roles ofemotions in cognitive instructional design, inmotivational design of instruction, in affectiveeducation, and in emotional education are brieflyoutlined. All these approaches do not consider how anyinstruction should be designed to become emotionallypositive for students. Within the presented frameworkof Emotional Design of Instruction (EDI) a set ofprescriptive propositions is obtained from a review ofconcepts, theories, and empirical findings in thefield of research on emotion. Five major dimensions ofinstructional relevant emotions are identified: feararising from judging a situation as threatening, envyresulting from the desire to get or not to losesomething, anger coming from being hindered to reacha goal, sympathy as an experience in relation to otherpeople who are in the need of help, and pleasure basedon mastering a situation with a deep devotion. Theauthor describes twenty instructional strategies thatcan be used to decrease negative feelings (fear, envy,and anger) and to increase positive feelings (sympathyand pleasure) during instruction. The article closeswith a discussion of theoretical shortcomings and openquestions concerning research and practicalapplications.