Complex Problem-solving Abilities Research Articles

Exploring the Predictive Potential of Complex Problem-Solving in Computing Education: A Case Study in the Introductory Programming Course

Programming is acknowledged widely as a cornerstone skill in Computer Science education. Despite significant efforts to refine teaching methodologies, a segment of students is still at risk of failing programming courses. It is crucial to identify potentially struggling students at risk of underperforming or academic failure. This study explores the predictive potential of students’ problem-solving skills through dynamic, domain-independent, complex problem-solving assessment. To evaluate the predictive potential of complex problem-solving empirically, a case study with 122 participants was conducted in the undergraduate Introductory Programming Course at the University of Maribor, Slovenia. A latent variable approach was employed to examine the associations. The study results showed that complex problem-solving has a strong positive effect on performance in Introductory Programming Courses. According to the results of structural equation modeling, 64% of the variance in programming performance is explained by complex problem-solving ability. Our findings indicate that complex problem-solving performance could serve as a significant, cognitive, dynamic predictor, applicable to the Introductory Programming Course. Moreover, we present evidence that the demonstrated approach could also be used to predict success in the broader computing education community, including K-12, and the wider education landscape. Apart from predictive potential, our results suggest that valid and reliable instruments for assessing complex problem-solving could also be used for assessing general-purpose, domain-independent problem-solving skills in computing education. Likewise, the results confirmed the positive effect of previous programming experience on programming performance. On the other hand, there was no significant direct effect of performance in High School mathematics on Introductory Programming.

Analysis of the Exploration of Hematology Integration Education Model Guided by “Social Learning Theory”

Diseases of the blood system are highly complicated and involve many aspects. This article aimed to put forward the necessity of integrating quality and professional education, the necessity of learning logic, and the importance of establishing an integrated medical education model when teaching about blood system diseases. According to the requirements of the new medical science, this article puts forward the integration of an “online + offline + clinical” medical education mode based on the social learning theory and a learning evaluation mode based on medical literacy. The fundamental task of cultivating human beings into clinical medical talents with professional ethics, independent learning abilities, interpersonal communication abilities, complex problem-solving abilities, innovative consciousness, and critical thinking was implemented. This article aims to propose an improved construction plan to create a new and improved teaching system in medicine.

DEVELOPMENT OF COMPLEX PROBLEM SOLVING TESTS IN NEWTON’S DYNAMIC FOR HIGH SCHOOL

Complex problem solving (CPS) is important for students, but educators rarely provide tests that hone CPS, because the available tests are not up to standard. The aim of the study was to develop a physics test instrument based on CPS for high school that met the qualifications in the aspects of validity, reliability, discriminatory power and level of difficulty. The type of research used is the Borg & Gall research and development (R&D) model with 9 stages, namely: (1) preliminary study of problem-solving test instruments, (2) planning of instrument development and testing, (3) development of problem-solving-based test instruments, (4) initial field test by 5 material, construction and language expert validators, (5) main product revision, (6) main field test on 20 test takers, (7) operational product revision, (8) operational field test on 100 participants tests, and (9) final product revisions. This research uses a mixed method approach with qualitative and quantitative analysis. The developed test instrument consists of 15 essay test items. The results of the initial field test obtained 30 valid test items with minor revisions on the material, construction and language aspects. The results of the main field test obtained 14 test items valid and very reliable. The operational field test results obtained 14 valid and very reliable test items so that the CPS test is suitable for use and can be useful in helping educators to hone students' CPS ability.

Mobile Virtual Field Trip and Geography Education: Potential Exploration of Complex Problem Solving and Spatial Intelligence Capabilities

This study aims to determine the effect of M-VGFT media on students' Complex Problem Solving and Spatial Intelligence abilities. Quasi-experimental research with post-test-only group design method. The design used two experimental groups with a purposive or judgment sampling method considering the average class value. X.2 Social Science group (14 boys and 20 girls), X.3 Social Science group (17 boys and 17 girls), and X.4 Social Science (17 boys and 17 girls). The subjects of this study were first-year high school students in the even semester of the 2021/2022 academic year. Hypothesis testing using independent sample t-test to determine the effect of M-VGFT media on the ability of students' Complex Problem Solving and Spatial Intelligence in the subject of Geography on Volcanism. The results showed a significant influence between M-VGFT media on students' Complex Problem Solving Ability and Spatial Intelligence in Madrasah Aliah Negeri Baru. However, there are differences in the influence between male and female students on the two variables.

The Sustainable Development of Psychological Education in Students’ Learning Concept in Physical Education Based on Machine Learning and the Internet of Things

Aim: This paper aims to enhance the emphasis of college physical education (P.E.) in the psychological education of P.E. students and provide a reference for the innovation of P.E. teaching methods. Methodology and procedures: On the basis of the Internet of Things (IoT) and a deep-learning algorithm, combined with psychological education, the teaching effect and the influence on learning philosophy are comprehensively evaluated through the construction of teaching evaluation index system for college P.E. students. Results: The theoretical courses of P.E. students in colleges and universities lack the integration of psychological-education concepts. It is found that the new teaching mode not only has a significant effect on improvement of training courses, but also promotes learning enthusiasm and theoretical courses. In the aspect of psychological quality evaluation, emotional-control ability significantly improved, the average score increased from below 60 to above 79, and self-challenge ability and adaptability to adversity also effectively improved. In the evaluation of deep-learning ability, students’ critical thinking ability improved most obviously, and their complex problem-solving ability also improved to some extent. Conclusions: Based on the IoT and machine learning, college P.E. teaching mode can effectively improve students’ psychological quality and ability, effectively improve students’ training and theoretical achievements, and significantly improve their academic achievements. It can also improve students’ self-learning ability. Practical applications: This paper reforms the traditional P.E. teaching mode, effectively demonstrates the hypothesis through practical teaching, designs the teaching evaluation index system of college P.E. students, and improves their learning ability and comprehensive achievements.

Personality traits and complex problem solving: Personality disorders and their effects on complex problem-solving ability.

Complex problem solving (CPS) can be interpreted as the number of psychological mechanisms that allow us to reach our targets in difficult situations, that can be classified as complex, dynamic, non-transparent, interconnected, and multilayered, and also polytelic. The previous results demonstrated associations between the personality dimensions neuroticism, conscientiousness, and extraversion and problem-solving performance. However, there are no studies dealing with personality disorders in connection with CPS skills. Therefore, the current study examines a clinical sample consisting of people with personality and/or depressive disorders. As we have data for all the potential personality disorders and also data from each patient regarding to potential depression, we meet the whole range from healthy to impaired for each personality disorder and for depression. We make use of a unique operationalization: CPS was surveyed in a simulation game, making use of the microworld approach. This study was designed to investigate the hypothesis that personality traits are related to CPS performance. Results show that schizotypal, histrionic, dependent, and depressive persons are less likely to successfully solve problems, while persons having the additional behavioral characteristics of resilience, action orientation, and motivation for creation are more likely to successfully solve complex problems.

Energy Efficient End Device Aware Solution Through SDN in Edge-Cloud Platform

Recently, the networking industries have gone through tremendous changes. It demands high-speed operations and complex problem-solving abilities. To manage these evolutions Internet-of-Things (IoT) is a proposed solution from several technical corners. Numerous researchers and government organizations showing their interest to provide solutions with IoT implementation. Handling a huge amount of network data, its privacy and security, Quality of Service (QoS) requirements and heterogeneity of underlying networking components are the various challenges in IoT implementations. To provide the solution, Software Defined Networking (SDN) is becoming a bliss in managing such complex networking problems. The allocation of the Virtual Machines (VMs) into the end device is an NP-Hard combinatorial optimization problem. We formulate the problem by using simple Additive Weighing (SAW) or Weighted Sum Method (WSM) to allocate the VMs asymmetrically based on CPU Utilization and Memory usage to optimize the energy. The proposed algorithm ServerCons minimizes the number of live migrations and the number of nodes used as well as the energy usage is at par with the state of art algorithms such as First-Fit-Decreasing(FFD), Best-Fit-Decreasing (BFD), and Modified-Best-Fit-Decreasing (MBFD).

Investigating spatial impact on indoor personal thermal comfort

Thermal comfort prediction is essential for both maintaining a favorable indoor environment and reducing energy consumption. Predicted Mean Vote (PMV), as the most popular research method, has a limitation in processing various complex parameters and investigating the individual difference in occupants' thermal preference. Therefore, machine learning (ML) method has been utilized in exploring the personal thermal comfort prediction because of its strong self-study ability, high-speed computing ability, and complex problem-solving ability. However, the primary variables considered in previous studies focus on the human body's physiological and psychological aspects, while lack of considering architectural spatial impact, which causes different indoor microclimate. Therefore, the present research proposed a methodology to investigate the impact from spatial parameters on personal thermal comfort prediction model accuracy by developing an ANN-based model and explicitly representing the spatial variables in the model. The spatial parameters were identified and classified into buildings' spatial features, indoor spatial features and individual spatial features. The data required in developing the ANN-based model were collected by various field experiments. A baseline of model prediction accuracy was calculated by using conventional parameters, including personal-dependent parameters and environmental parameters. It was found that the spatial parameters had a noticeable impact on model prediction accuracies. By considering spatial parameters in the ANN-based model development, the prediction accuracies had been increased significantly compared with the conventional models.

The future of intelligence: The role of specific abilities

This contribution to the Special Issue on the Future of Intelligence focuses on specific abilities. Specific abilities are distinct abilities (e.g., math, verbal, spatial) that differ conceptually and empirically from general intelligence (g; variance common to tests). Two specific abilities are discussed: (a) ability tilt, defined as an ability pattern based on differences in math and verbal scores on standardized tests (e.g., SAT and ACT), yielding math tilt (math > verbal) and verbal tilt (verbal > tilt); and (b) complex problem solving (CPS), defined as the ability to adapt to novel and dynamically changing environments. Although g is generally a strong predictor of life outcomes, tilt and CPS predict criteria beyond g. Math tilt predicts science, technology, engineering, and math (STEM) criteria (e.g., jobs, majors, aptitudes), whereas verbal tilt predicts humanities criteria. Similarly, CPS abilities (e.g., knowledge acquisition and knowledge application) predict academic achievement and job performance after controlling for g. CPS and tilt can be integrated into theories of intelligence such as Cattell-Horn-Carroll theory and investment theories. Future research should consider (a) reporting norms for tilt and CPS in testing programs (e.g., SAT and ACT); (b) using tilt and CPS to reduce disparate impact between groups; (c) using computerized testing to track tilt and CPS over time; and (d) integrating tilt and CPS into trait complexes involving personality traits, vocational interests, and specific abilities.

Effect of Mobile-Augmented Reality (MAR) in Digital Encyclopedia on The Complex Problem Solving and Attitudes of Undergraduate Student

The study aimed to determine the effect of mobile aug-mented reality in the digital encyclopedia on complex problem-solving ability and responsible decision making attitude of first-year students. The research was a quasi-experiment (quantitative research) with pre-test and posttest methods. The population was first-year students of 2019/2020 in the Geography Education program, Faculty of Social Sci-ences, State University of Malang. The experimental group was from the PGEO6006-L class, and the control group was from the PGEO6006-A class, totaled 73 participants. Data were collected using a qualitative method using interviews and a quantitative method using a questionnaire for 4 weeks. The data analysis used an independent t-test to determine the effect of mobile augmented reality on students' complex problem-solving ability and responsible decision making atti-tude in Cosmography class. The results indicated that mobile augment-ed reality in the digital encyclopedia has a significant effect on stu-dents' complex problem-solving ability and responsible decision mak-ing attitude.

Complexity, Design and Culture: convergence for digital experiences

The design of digital experiences, systems and sociotechnical solutions in the contemporary society is characterized by increasing complexity. More and more variables are present and unpredictable effects are observed. The complexity of systems calls for awareness and complex problem-solving ability. In this survey, design in general, and design of the user experience in particular, is placed in the scope of complexity. Moreover, the design reasoning and methods are sensitive to culturally determined factors and cognitive styles. This survey converges complexity and complex problem solving, design of the user experience and cultural variations, reviewing bodies of work to suggest and map interdependencies that will shape future challenges in systems design.

Improving student’s complex problem solving through LAPS-Talk-Ball learning integrated with interactive games

One of the competencies needed currently is non-routine competency in solving a complex problem. Students with complex problem solving will be able to identify the complex problem, understand to review related information, and create solutions for the problems. However, the improvement of complex problem-solving abilities lacks attention. This phenomenon occurs when students are given problem-solving, students have not been able to understand the problem, plan solutions, and provide solutions to problems. To overcome these problems, the LAPS-Talk-Ball learning model integrated with interactive games that guide the students in solving problems was implemented by using interactive games and speech balloons to stimulate each student to express their ideas. This research is quantitative experimental research to determine the success of the LAPS-Talk-Ball learning model integrated with android-based interactive games in training complex problem-solving students using saturated sampling and non-equivalent control group research designs. From the results of gain test results high improvement category, which means that the LAPS-Talk-Ball learning model integrated with android-based interactive games can train the complex problem-solving abilities of students.

First among equals: Log data indicates ability differences despite equal scores

Analyzing test-taking behavior allows researchers to investigate the steps and actions resulting in the specific test outcome. The underlying assumption is that test-taking behavior is a valid indicator of the tested ability. The aim of this paper is to scrutinize this assumption in the context of complex problem solving (CPS) by analyzing individual differences in test-taking behavior and their relation to individual differences in established correlates of CPS ability. We investigated a sample of Finnish students who achieved the maximum score on five CPS tasks and worked on an additional intelligence measure. We logged the number of interactions with the CPS tasks and time-on-task. Students showed significant variance in both time-on-task (s2 = 260.09, p = .005) and the number of interactions (s2 = 0.381, p < .001) despite having no variance in their CPS test scores. Using structural equation modeling, both time-on-task (β = 0.17, p = .015) and the number of interactions (β = −0.17, p < .001) significantly predicted students' GPA, which has frequently shown to be associated with CPS ability. When adding intelligence – another established correlate of CPS – as predictor of GPA (β = 0.55, p < .001), the relation both between time-on-task (β = −0.09, p = .053) and the number of interactions (β = 0.09, p = .100) and students’ GPA was reduced to negligible effects. Taken together, our study supports the assumption of test-taking behavior actually representing differences in CPS ability – over and above test scores.

Neuropsychological Performance 1 Year After Status Epilepticus in Adults.

Status epilepticus (SE) may lead to or worsen cognitive dysfunction. Few studies have evaluated magnitude and profile of cognitive dysfunction in patients after SE. Characterization of cognitive deficits may be important for rehabilitation and follow-up. We assessed cognitive function in a consecutive, non-selected group of relatively healthy survivors with a comprehensive neuropsychological test battery. A total of 33 patients (24 men, 9 women; mean age 54,9years, mean education 11,8years) were tested 1year after SE with Wechsler Adult Intelligence Scale Fourth edition (WAIS-IV), Rey Auditory Verbal Learning Test, subtests from the Wechsler Memory Scale-Revised, Phonemic and Semantic word list generation, and the Halstead-Reitan Battery. Premorbid IQ was estimated with a Norwegian version of the National Adult Reading Test (NART). Results were compared to published norms. Regression analyses and independent groups t-tests were performed to assess the influence of background variables. Mean performance generally was about one standard deviation below average. Full scale IQ (WAIS-IV) was significantly reduced compared to estimated premorbid IQ (NART). Negative influence on cognition of brain lesions visible on computed tomography or magnetic resonance imaging and duration of SE >30min was shown by group comparisons. SE represents a marker for possible cognitive dysfunction, and follow-up with neuropsychological assessment and cognitive rehabilitation seems warranted in most patients. Complex problem-solving abilities with high general sensitivity to brain impairment showed the most prominent reduction. Otherwise, no specific profile of domain affection was found. Structural brain lesions and duration of SE over 30min represent risk factors for cognitive deficit.

Bounded interdisciplinarity: critical interdisciplinary perspectives on context and evidence in behavioural public policies

AbstractA behavioural public policy movement has flourished within the global policy realm. While this movement has been deemed interdisciplinary, incorporating behavioural science theories and methods in a neoclassical economics-governed policy process, this paper analyses the bounded form of interdisciplinarity that characterizes it. We claim that an engagement is missing with the broader sweep of social sciences, which share similar concerns but deploy different analytical perspectives from those of behavioural public policy. Focusing on two central concepts (context and evidence), we aim to show how behavioural public policy's bounded interdisciplinarity implies constrained understandings of context and evidence, thereby limiting its complex problem-solving abilities. At the same time, we highlight some alternative examples of behavioural public policy practice that do explore new critical interdisciplinary horizons.

Konferensi Nasional Penelitian Matematika dan Pembelajarannya

PREFACEThe National Conference on Mathematics and Learning Research or KNPMP is an annual scientific forum organized by the Mathematics Education Study Program of Muhammadiyah University of Surakarta since 2016, and this year for the fourth time the KNPMP is held with the theme “Enhancing Mathematical Literacy to Meet the 4.0 Industrial Revolution”. This theme is based on a global challenge, namely the challenge of the Industrial Revolution 4.0 era. where according to Klaus Schwab, founder of the World Economic Forum, “Industrial Revolution 4.0. is an era that is blurring the lines between the physical, digital, and biological spheres.” Which means that humans are physically and ideally integrated with today’s digital technologies such as artificial intelligent technology, autonomous vehicles, and internet of things. To deal with this, Alex Gray stated that complex problem-solving abilities are the most important skills that must be possessed. In the context of mathematics, the increase in mathematical literacy skills is important to do because the complex problem-solving aspect is one of the procedures in it. Therefore, the theme “Enhancing Mathematics Literacy to Meet the 4.0 Industrial Revolution” becomes very relevant to be appointed to the IV KNPMP this year.In accordance with the theme, we invited one expert as the keynote speaker, and two experts as the main speakers. As the keynote speaker, we invite Prof. Agus Subekti, Ph.D. as Chair of the 2019 DRPM Research Guidance Team of the Ministry of Research, Technology and Higher Education and also a Professor at the University of Jember. As the first speaker was Prof. Dr. Basuki Widodo, M.Sc., President of the Indonesian Mathematics Society or IndoMS and a Professor in the Institut Teknologi Sepuluh November. As the second speaker, Prof. Dr. Yus M. Cholilly, a Professor of Mathematics Education at the Universitas Muhammadiyah Malang.I would like to express my gratitude to everyone who have supported this conference. I hope that the conference will facilitate a fruitful discussion, connect you with the other experts in the field, and further initiate a strong collaboration.List of Organizing Committee and Conference Documentation are available in this pdf.

Statistical Analysis of Complex Problem-Solving Process Data: An Event History Analysis Approach.

Complex problem-solving (CPS) ability has been recognized as a central 21st century skill. Individuals' processes of solving crucial complex problems may contain substantial information about their CPS ability. In this paper, we consider the prediction of duration and final outcome (i.e., success/failure) of solving a complex problem during task completion process, by making use of process data recorded in computer log files. Solving this problem may help answer questions like “how much information about an individual's CPS ability is contained in the process data?,” “what CPS patterns will yield a higher chance of success?,” and “what CPS patterns predict the remaining time for task completion?” We propose an event history analysis model for this prediction problem. The trained prediction model may provide us a better understanding of individuals' problem-solving patterns, which may eventually lead to a good design of automated interventions (e.g., providing hints) for the training of CPS ability. A real data example from the 2012 Programme for International Student Assessment (PISA) is provided for illustration.

Latent Class Analysis of Recurrent Events in Problem-Solving Items.

Computer-based assessment of complex problem-solving abilities is becoming more and more popular. In such an assessment, the entire problem-solving process of an examinee is recorded, providing detailed information about the individual, such as behavioral patterns, speed, and learning trajectory. The problem-solving processes are recorded in a computer log file which is a time-stamped documentation of events related to task completion. As opposed to cross-sectional response data from traditional tests, process data in log files are massive and irregularly structured, calling for effective exploratory data analysis methods. Motivated by a specific complex problem-solving item "Climate Control" in the 2012 Programme for International Student Assessment, the authors propose a latent class analysis approach to analyzing the events occurred in the problem-solving processes. The exploratory latent class analysis yields meaningful latent classes. Simulation studies are conducted to evaluate the proposed approach.

Fluid Ability (Gf) and Complex Problem Solving (CPS).

Complex problem solving (CPS) has emerged over the past several decades as an important construct in education and in the workforce. We examine the relationship between CPS and general fluid ability (Gf) both conceptually and empirically. A review of definitions of the two factors, prototypical tasks, and the information processing analyses of performance on those tasks suggest considerable conceptual overlap. We review three definitions of CPS: a general definition emerging from the human problem solving literature; a more specialized definition from the “German School” emphasizing performance in many-variable microworlds, with high domain-knowledge requirements; and a third definition based on performance in Minimal Complex Systems (MCS), with fewer variables and reduced knowledge requirements. We find a correlation of 0.86 between expert ratings of the importance of CPS and Gf across 691 occupations in the O*NET database. We find evidence that employers value both Gf and CPS skills, but CPS skills more highly, even after controlling for the importance of domain knowledge. We suggest that this may be due to CPS requiring not just cognitive ability but additionally skill in applying that ability in domains. We suggest that a fruitful future direction is to explore the importance of domain knowledge in CPS.

When Less Is Less: Solving Multiple Simple Problems Is Not Complex Problem Solving-A comment on Greiff et al. (2015).

In this commentary, we critically review the study of Greiff, Stadler, Sonnleitner, Wolff, and Martin, “Sometimes less is more: Comparing the validity of complex problem solving measures” (Intelligence, 2015, 50, 100–113). The main conclusion of Greiff et al. that the “multiple complex systems” (MCS) approach to measuring complex problem-solving ability possesses superior validity compared to classical microworld scenarios (“less is more”) seems to be an overgeneralization based on inappropriate analysis and selective interpretation of results. In its original form, MCS is a useful tool for investigating specific aspects of problem solving within dynamic systems. However, its value as an instrument for the assessment of complex problem solving ability remains limited.