General Problem-solving Ability Research Articles

General Problem-solving Skills Can be Enhanced by Short-time Use of Problem-Based Learning (PBL)

Future students are confronted with a complex world that demands the ability to solve problems in unstructured, undefined, and unfamiliar situations. The aim of the present study was to investigate the development of problem-solving skills through the implementation of Problem-Based Learning (PBL). While previous research has primarily focused on content-related and long-term measurements when examining the effects of PBL, this study took a different approach by exploring the general increase in problem-solving skills resulting from PBL. The sample consisted of 90 second-semester students who were assessed at three different time points using three subscales of the Wilde-Intelligenz-Test I & II: analogies (AL), letter series (BR) and numerical series (ZN). The findings revealed a significant improvement in general problem-solving abilities within the PBL group. These results provide valuable insights into the impact of PBL on the development of general problem-solving skills, even within a domain-independent and short-term context. Lecturers are encouraged to consider implementing PBL in their study programs, as it equips graduates with the necessary skills to tackle the challenges of today’s dynamic and constantly changing world.

Has OpenAI Achieved Artificial General Intelligence in ChatGPT?

In this paper, we present an analysis of ChatGPT, a language model developed by OpenAI, through the lens of Human-Centric Functional Modeling (HCFM). ChatGPT is designed to interact through a chat interface in a conversational manner, with the ability to answer follow-up questions, admit mistakes, challenge incorrect premises, and reject inappropriate requests. Since HCFM is hypothesized to provide a functional model for assessing the existence and magnitude of general problem-solving ability (intelligence), and since according to ChatGPT itself HCFM is the only such functional model in existence, the purpose of the paper is to demonstrate the usefulness of HCFM in determining whether an AI like ChatGPT is an AGI. Using Human-Centric Functional Modeling, we aim to determine whether ChatGPT exhibits narrow problem-solving ability, classifying it as an artificial intelligence (AI), or whether it exhibits general problem-solving ability, classifying it as AGI. We also consider the magnitude of ChatGPT's problem-solving ability within the conceptual space defined by HCFM. Finally, this paper also explores the issue from the perspective of the “collective social brain” hypothesis, which predicts which AI behavior the majority of humans will find to be intelligent, as well as predicting that true machine intelligence lies outside such narrow human definitions of intelligent behavior.

Dual Differential Grouping: A More General Decomposition Method for Large-Scale Optimization.

Cooperative coevolution (CC) algorithms based on variable decomposition methods are efficient in solving large-scale optimization problems (LSOPs). However, many decomposition methods, such as the differential grouping (DG) method and its variants, are based on the theorem of function additively separable, which may not work well on problems that are not additively separable and will result in a bottleneck for CC to solve various LSOPs. This deficiency motivates us to study how the decomposition method can decompose more kinds of separable functions, such as the multiplicatively separable function, to improve the general problem-solving ability of CC on LSOPs. With this concern, this article makes the first attempt to decompose multiplicatively separable functions and proposes a novel method called dual DG (DDG) for better LSOP decomposition and optimization. The novelty and advantage of DDG are that it can be suitable for not only additively separable functions but also multiplicatively separable functions, which can considerably expand the application scope of CC. In this article, we will first define the multiplicatively separable function, and then mathematically show its relationship to the additively separable function and how they can be transformed into each other. Based on this, the DDG can use two kinds of differences to detect the separable structure of both additively and multiplicatively separable functions. In addition, the time complexity of DDG is analyzed and a DDG-based CC algorithm framework is developed for solving LSOPs. To verify the superiority of DDG, experiments and comparisons with some state-of-the-art and champion algorithms are conducted not only on 30 LSOPs based on the test suite of the IEEE CEC large-scale global optimization competition, but also on a case study of the parameter optimization for a neural network-based application.

Human-Centric Functional Computing as an Approach to Human-Like Computation

This paper makes the argument that the hypothetical “functional state space”-based computing model described in this paper is a fundamentally new and different approach of disruptive importance. This paper argues that all functional states in any given functional state space can be reached in terms of a set of basic operations like in the case of other state spaces. The difference is that the functional states are separated in this space by a “semantic distance” that reflects their similarity, and that separating functional states according to similarity in this way introduces the possibility of generalizing between one functional state and another. Assuming all problems in understanding any system modeled in terms of functional state space can be represented as the lack of a path from one functional state to another, and that all solutions are represented by the processes within the system that allow the system to transition between the two functional states, then general problem-solving ability with respect to any system is potentially represented by the volume of its functional state space that can be navigated per unit time to find a solution, multiplied by the density of functional state space that has to be navigated through to find it. Simple geometric arguments in functional state space suggest that this separation of functional states by semantic distance in turn creates the possibility of exponentially increasing ability to solve problems in understanding the system when the ability to generalize can be increased until it spans the entire functional state space. Assuming the human cognitive system can be represented as operating within a functional state space (the so-called “conceptual space”), and assuming that this conceptual space is a complete semantic model of concepts and reasoning, then by computing in terms of paths providing a complete representation of the meaning of reasoning, where those navigate through that space between functional states providing a complete representation of the meaning of their underlying concepts, any individual or collective artificial cognition might potentially transfer meaning rather than just information at vastly greater speed and scale. The ability to transfer meaning suggests that one of the problems that computing based on functional state spaces deduced through Human-Centric Functional Modeling (or so-called “Human-Centric Functional Computing”) might exponentially increase the capacity to solve is the problem of automatically generalizing computing solutions in order to reuse them in solving other problems where they apply, and doing so at vastly greater speed and scale without the need for human reprogramming.

Metacognition and fluid intelligence in value-directed remembering

The ability to selectively focus on and remember important information, referred to as value-directed remembering, may be crucial for effective memory functioning. In the present study, we investigated the relationships between metacognitive monitoring and control accuracy, selectivity for valuable information, and fluid intelligence. Mediation analyses demonstrated that participants’ monitoring assessments and later recall were influenced by the value of the to-be-learned words and the accuracy of participants’ judgments was moderated by fluid intelligence. Moreover, recall, selectivity, metacognitive awareness of selectivity, and metacognitive accuracy all generally increased with task experience, demonstrating participants’ ability to improve their memory by utilizing cognitive resources more effectively. Together the results suggest that people may be aware of the need to be selective, and engaging in value-directed remembering may be related to higher-level cognitive skills associated with problem-solving and reasoning. Specifically, the strategic use of memory may be involved in focusing on important information, and the metacognitive processes that allow for this prioritization of memory may be related to more general problem-solving abilities that involve identifying important features of information to guide cognition in a broader context.

Defining a continuum from individual, to swarm, to collective intelligence, and to general collective intelligence

The concept of collective intelligence (CI) is distinguished from the concept of a general collective intelligence or GCI in that CI addresses a specific range of problems and therefore has narrow problem-solving ability, while GCI can potentially address any problem and therefore has general problem-solving ability. General problem-solving ability in groups has been represented by a GCI factor c defined in analogy with the individual intelligence factor g which measures IQ. While groups might have an innate GCI factor, GCI is a hypothetical platform that combines groups into a virtual collective cognition with a single well-defined thread of collective reasoning having general problem-solving ability, thereby creating an artificial c factor separate from the innate c factor. GCI also creates the opportunity to exponentially increase this c factor. This paper explores the differences between various forms of innate or artificial, as well as individual and group cognition.

The effects of empathy and self concept on problem solving: Focusing on the mediating effect of communication of nursing students

Purpose: This study aimed to identify the effect of nursing students' empathy and self-esteem on problem-solving ability through the mediated effect of communication skills.Methods: Data were collected from 179 nursing students between 1 October and 30 October, 2018 from three universities in Gyeonggi-Do. Data on subjects’ general characteristics, empathy, self-esteem, communication skills, and problem solving ability were collected using a structured self-reporting questionnaire.Results: Of the total subjects, 89.9% were female. The subjects' empathy was 3.40±0.36, self-esteem was 3.16±0.56, communication skills was 3.65±0.48, and problem-solving ability was 3.52±0.42. Communication skills showed a partial mediating effect on the relationship between empathy and problem solving ability. Communication skills also showed a partial mediating effect on the relationship between self-esteem and problem solving ability.Conclusion: These findings indicate that it will be more effective to develop strategies that can improve empathy, self-esteem, and communication skills together when developing programs that improve problem-solving ability.

Effectiveness of problem-based learning method on nursing teaching in higher vocational schools

Objective To assess the application effectiveness of problem-based learning (PBL) on nursing teaching in higher vocational schools. Methods A total of 204 nursing students, including 76 students from Grade 2017 and 128 students from Grade 2018, were selected and divided into the observation group, which included 41 students from Grade 2017 and 74 students from Grade 2018 receiving PBL method, and the control group, which encompassed the others receiving traditional teaching methods. General self-efficacy feelings and problem-solving abilities of students in different grade in two groups were assessed at the beginning and the end of the term. Results The scores of general self-efficacy feeling between the observation group and the control group at the beginning of the term showed no statistically significant differences (P>0.05); the scores of general self-efficacy feeling at the end of the term in the observation group were obviously higher than those in the control group [(32.21±4.17) vs. (28.25±3.74), t=7.067, P<0.01]. The scores in Grade 2017 and Grade 2018 in the observation group were (32.61±3.79) and (31.99±4.38), respectively, which were significantly higher than those in the control group in Grade 2017 (27.45±3.95) and in Grade 2018 (28.74±3.55) at the end of the term. The scores of problem-solving abilities in Grade 2017 and Grade 2018 in the observation group were (25.35±2.78) and (25.99±3.19), respectively, which were significantly higher than those in the control group [(21.34±2.92), (21.30±3.51), t=6.064, t=7.679, P<0.01, P<0.01]. Conclusion PBL method in nursing students can effectively improve the general self-efficacy feeling and problem-solving ability of students, which is worthy of promotion in higher vocational schools. Key words: Problem-based learning; Nursing education in higher vocational schools; General self-efficacy feeling; Problem-solving ability

Students’ Ability to Apply Their Knowledge in a Gaming Exercise: An Exploratory Study

ABSTRACTThis paper reports on a study exploring master students’ ability to apply their knowledge when solving an internal pricing problem in a supply chain. Analyses of 33 negotiation progress reports and 8 recordings of discussions demonstrate that most of the students were not able to apply relevant concepts and models to guide their handling of the task. The main reason for the variability in performance was the significant differences in the students’ general problem-solving abilities. Most students did not perform a thorough task analysis or develop a solution strategy before they met for negotiations, and they did not keep track of their task performance. The implications for teaching are highlighted, and directions for further research are proposed.

Student effort expectations and their learning in first-year introductory physics: A case study in Thailand

The Maryland Physics Expectations (MPEX) survey was designed to probe students' expectations about their understanding of the process of learning physics and the structure of physics knowledge---cognitive expectations. This survey was administered to first-year university students in Thailand in the first semester of an introductory calculus-based physics course during academic years 2007 and 2008, to assess their expectations at the beginning of the course. The precourse MPEX results were compared and correlated with two separate measures of student learning: (1) individual students' normalized gains from pre and post Force and Motion Conceptual Evaluation (FMCE) results, which measure students' conceptual understanding, and (2) student's scores on the final exam, which measure their more general problem-solving ability. The results showed a significant positive correlation between their overall MPEX score and five of the six MPEX cluster scores, with their normalized learning gains on the FMCE for both academic years. The results also showed significant positive correlations between student MPEX scores and their final exam scores for the overall MPEX score and all MPEX cluster scores except for the effort cluster. We interviewed two groups of five students each, one group with small favorable scores on the precourse MPEX effort cluster and one with high favorable scores on the precourse MPEX effort cluster, to see how the students' learning efforts compared with their MPEX results. We concluded from the interviews that what the students think or expect about the MPEX effort involved in learning physics does not match what they actually do.

Digital game building: learning in a participatory culture

Background: The emergence of a participatory culture, brought about mainly by the use of Web2.0 technology, is challenging us to reconsider aspects of teaching and learning. Adapting the learning-as-digital-game-building approach, this paper explores how new educational practices can help students build skills for the 21st century. Purpose: This paper examines elementary students' learning experiences through digital game building and playing. The following research questions guided the study: (1) What emotions do students experience during the process of building digital games for others to use?; (2) What traits do students display when they learn through digital game-building?; and (3) What do students learn from the digital game-building experience? Sample: The participants were 21 elementary students (19 boys and two girls), aged between seven and 11, who were on a summer camp at a university in Canada. Design and methods: This small-scale study made use of enactivism (Li, Clark, and Winchester, Instructional design and technology grounded in enactivism: A paradigm shift?, British Journal of Educational Technology 41, no. 3: 403–419, 2010), a new theoretical framework, as a basis for analysing the students' experiences and responses as they created computer games to teach others the concept of Issac Newton's Three Laws of Motion. Quantitative and qualitative data collected included student and parent surveys, teacher and student interviews, field observations and the digital games created by the students. Data were subjected to quantitative and thematic analyses. Results: The results indicated that only a small minority of students reported never feeling the positive emotions excited/happy or smart/proud during the process of building digital games. In addition, analysis suggested that creativity, engagement and new identity were the three salient traits displayed by the students when learning by digital game-building. There was also evidence that students increased their understanding of the subject matter in question (mathematics, science and technology) and enhanced their general problem-solving abilities through the process. Conclusions: This small-scale study suggests that student engagement in the game-building experience can enhance not just the learning of the game design process but also subject matter and generic skills. Thus, the learning-as-building approach can empower students to ‘take over the technology’ and become creators rather than passive consumers.

Ross Programme: Effectiveness with Young People in Residential Childcare

The objective of this study was to assess the efficacy of the Ross Programme for adolescent boys within residential care, which was developed to tackle antisocial behaviour and is based on a cognitive behavioural model. The main aim of the programme is to teach participants skills and values that are required for prosocial competence and that are antagonistic to antisocial behaviour. The study was designed to compare, on a variety of measures, a group of adolescents who completed the Ross Programme with a group who had not yet started the programme. Significant effects of the programme on level of risk of general re-offending, problem-solving ability, behaviour, and criminal attitudes were observed for the treatment group. In comparison, the control group showed no significant improvements in any of the treatment outcome measures.

Common-Measures Bias in the Balanced Scorecard: Cognitive Effort and General Problem-Solving Ability

Lipe and Salterio (2000) found decision-makers role-playing as superiors in a performance evaluation setting placed 97 percent of criteria weight on information common to subordinates in making their judgments when evaluating performance using the Balanced Scorecard (BSC). Because theory underlying the BSC (Kaplan and Norton 1996, 2001) assumes all measures included are relevant for achieving organizational objectives, several subsequent studies have attempted to understand or alleviate this common-measures bias (e.g., Krumwiede et al. 2002; Banker et al. 2004; Libby et al. 2004; Roberts et al. 2004; Dilla and Steinbart 2005). This study is the first to directly test the effort-based explanation of the common-measures bias (Slovic and MacPhillamy 1974, Lipe and Salterio 2000). Additionally, this research investigates the role of participants' problem-solving ability on mitigating the common-measures bias. Results show strong support for the effect of problem-solving ability on increased measures usage, but not for the effort-based explanation. Superiors who relied more on common measures were less convinced subordinates should be evaluated with different measures and less convinced that subordinates' target markets were unique. These results raise important questions about the nature of common-measures bias.

Evolution of spatial cognition: mechanisms and tasks

Finding places and orienting in space are among the most basic behavioral performances of all mobile animals. In many different species, simple mechanisms have been identified for recognizing places, returning to a “home” after an excursion, or concatenating simple navigational steps to longer routes. These mechanisms form the basis of human spatial performances. The talk will explore the relation between animal and human spatial cognition along two dimensions, (1) animal roots of human performances, and (2) features of spatial cognition uniquely found in humans. In the first part of the talk, we will present a novel behavioral paradigm for studying large-scale spatial performances in rats (Holscher in J Exp Biol 208:561–569, 2005). The setup consists of a running ball in a socket, hovering in a stream of air. The rat is fixated over the pole of the ball such that the rat’s legs touch the ball. Translatory movements of the rat are thus transformed into rotations of the ball about horizontal (“equatorial”) axes. Rotations of the rat about its vertical (dorso-ventral) axis are not restricted while the ball is prevented from such rotations by guiding wheels. In effect, then, the system is open-loop for translations and closed-loop for rotations of the rat. The rotation of the ball is measured by an optical system and used to control a virtual reality projection surrounding the rat on a panoramic screen. Initial results indicate that rats accept the virtual environment and run for long distances, up to several hundreds of meters. In addition, they can be trained to search for rewards at places specified by landmarks. In ongoing experiments, we investigate route behavior in rats, which is considered a stepping stone to topological navigation in humans. In the second part of the talk, we will discuss in what ways human spatial abilities differs from animal performances. We suggest that human spatial behavior, while relying on abilities found already in the animal wayfinding hierarchy (Trullier et al. in Prog Neurobiol 51:483–544, 1997), has taken advantage of two trends of the cognitive evolution in the social domain. First, communicating spatial knowledge, either face-to-face, by publishing or by sign-posting enables us to travel to almost every point in the world with a minimum of prior knowledge. Second, cooperative behavior in building cities, roads, and transportation systems shapes the environment in ways that may be more easy to navigate. We will argue that human wayfinding differs from animal behavior in the ability of utilizing these “socio-spatial” types of knowledge. The cognitive apparatus needed for these performances is not specifically spatial, but includes general problem solving and working memory abilities.

Clinical Competence: General Ability or Case-specific?

Before the 1970s, research into the development of clinical competence was mainly focused on general problem-solving abilities. The scope of research changed when Elstein and colleagues discovered that individual ability to solve clinical problems varies considerably across cases. It was concluded that problem solving abilities are highly dependent on domain-specific knowledge rather than on general problem solving skills. Elstein called this phenomenon "case specificity." The finding of content specificity will be contrasted with the existence of a general clinical problem solving ability, and the relationship between preclinical knowledge and a problem solving ability will be investigated. A correlation matrix was calculated with clerkship final scores from 10 disciplines to examine the magnitude of the interrelations. A confirmatory factor analysis was applied to the corresponding covariances using structural equation modeling to investigate whether scores on finals shared any common variance across clerkships. Finally, two additional models were tested to examine the nature of the relationship between preclinical knowledge and problem solving. Low to moderate correlations across clerkship disciplines were found, supporting the original findings of content specificity. Further investigation showed that in addition to specific knowledge, a general, content-independent ability is needed to perform on these examinations. Clinical competence, as measured in this study, is based on a combination of specific preclinical knowledge and a problem-solving ability. Case specificity fits perfectly well in this interactional perspective on clinical problem solving but does not explain it. The phenomenon "case specificity" is therefore not solely a result of content knowledge, but of level of experience and level of case difficulty.

Linguistic explanations of non-nativeness in second language acquisition and the critical period hypothesis

What type of explanation for persistent non-nativeness in older second language learners is compatible with the claim that L2 mental grammars are derived from Universal Grammar? This article argues that non-linguistic explanations, such as interference in the acquisition process by general problem-solving abilities or affective factors, are less plausible than accounts which claim that a subpart of Universal Grammar itself is subject to a critical period. It is further suggested that debate between proponents of the linguistic accounts is likely to lead to progress in understanding how the language faculty is involved in second language acquisition.

Relationships between problem-solving competencies and militaristic attitudes: Implications for peace education.

This study investigated relationships between problem-solving competencies and militaristic attitudes in a sample of 101 college students. Problem-solving competencies for international conflict resolution were assessed by using the Conflict Resolution Strategies Checklist to score essays written about how the President of the United States could respond to a hypothetical conflict between the United States and Iran. Scholastic Aptitude Test (SAT) Verbal scores and grades in Introductory Psychology classes were used as measures of more general problem-solving ability. Whereas checklist scores, SAT scores, and class grades were interrelated, measures of problem-solving competency were significantly correlated neither with militaristic attitudes nor with gun control attitudes, Religious/Social Authoritarianism, or Social Dominance Orientation. However, militaristic attitudes were related to Religious/Social Authoritarianism (r =. 45) and to Social Dominance Orientation (r =. 35). Implications for peace educa...

Generalized Versus Spouse-Specific Anger/Hostility and Men’s Violence Against Intimates

The present study examined the extent to which generalized versus spouse-specific anger/hostility was associated with partner violence in 263 men seeking conjoint marital therapy. Clinic men were classified as nonviolent (NV), moderately violent (MV), and severely violent (SV). A community comparison group of relationship-satisfied, non-violent men (CO) was also included. All clinic men reported higher levels of generalized and spouse-specific anger, spouse-specific aggression/hostility, depressive symptomatology and lower spouse-specific assertiveness than community men. SV men reported higher levels of spouse-specific anger/hostility, relationship discord, depressive symptomatology, and lower general problem-solving ability than NV men. Regression analyses confirmed that spouse-specific anger/hostility, low problem-solving ability, and relationship discord were significant predictors of men's violence. Overall, generalized anger and hostility were not unique predictors of men's violence against intimates.

Eye on the prize

In its early stages, the field of AI had as its main goal the invention of computer programs having the general problem-solving abilities of humans. Along the way, a major shift of emphasis developed from general-purpose programs toward performance programs, ones whose competence was highly specialized and limited to particular areas of expertise. In this article, I claim that AI is now at the beginning of another transition, one that will reinvigorate efforts to build programs of general, humanlike competence. These programs will use specialized performance programs as tools, much like humans do.

Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization

Expertise consists of rapid selection and application of compiled experience. Robust reasoning, however, requires adaptation to new contingencies and intelligent modification of past experience. And novel or creative reasoning, by its real nature, necessitates general problem-solving abilities unconstrained by past behavior. This article presents a comprehensive computational model of analogical (case-based) reasoning that transitions smoothly between case replay, case adaptation, and general problem solving, exploiting and modifying past experience when available and resorting to general problem-solving methods when required. Learning occurs by accumulation of new cases, especially in situations that required extensive problem solving, and by tuning the indexing structure of the memory model to retrieve progressively more appropriate cases. The derivational replay mechanism is discussed in some detail, and extensive results of the first full implementation are presented. These results show up to a large performance improvement in a simple transportation domain for structurally similar problems, and smaller improvements when less strict similarity metrics are used for problems that share partial structure in a process-job planning domain and in an extended version of the strips robot domain.