Problem-solving Protocols Research Articles

Effects of a Problem-Solving Team Consultation on the Problem-Solving Process: An Evaluation of Concept Knowledge, Implementation Integrity, and Student Outcomes

ABSTRACT We examined the outcomes of elementary school-based problem-solving teams (PSTs) who participated in a multi-component consultation focused on enhancing systematic problem solving. Consultation provided to each PST included training in the use of a problem-solving protocol (i.e., Outcomes: Planning Monitoring, and Evaluating [Outcomes: PME]) and subsequent performance feedback and training regarding team use of the protocol. A train-the-trainer model was used whereby researchers trained school psychologists to deliver the intervention and serve as coaches on the PSTs. At baseline, all three PSTs demonstrated low (i.e., <50%) problem-solving procedural integrity (i.e., the degree to which critical components of the problem-solving process were implemented), often scoring lowest for problem-solving components related to data collection and analysis (e.g., treatment integrity, progress monitoring, and pre-post intervention data). Each PST demonstrated increased problem-solving integrity following implementation of the first intervention component, problem-solving information and Outcomes: PME training. Procedural integrity also increased for the two PSTs that received the second intervention component, performance feedback. An increase in procedural integrity was not found for one PST that received the third intervention component, targeted consulting; however, data collection was incomplete. Results are discussed in terms of future research priorities to examine consultation that promotes the use of quality team problem-solving components.

Effectiveness of Family-centered Problem-solving Training on the Negative Effects of Social Distancing Caused by COVID-19 in the Family of Nurses of Kermanshah University of Medical Sciences

Background and Objective: The outbreak of the coronavirus disease 2019 (COVID-19) and the psychological consequences of social distancing are among the most important social events of the 21st century that have made nurses' families face their related psychological consequences. This study aimed to evaluate the effectiveness of family-centered problem-solving training on the negative effects of social distancing caused by COVID-19 in the families of nurses at Kermanshah University of Medical Sciences, Kermanshah, Iran. Materials and Methods: The present quasi-experimental study was conducted based on a pretest-posttest control group design. The statistical population (n=380) included the families of married nurses working in the COVID-19 ward of public hospitals in Kermanshah, 2020-2021. The sample consisted of the families of nurses who obtained a high score in the social distance measurement tool; accordingly, 100 people were included in the study and assigned to two groups of intervention and control (n=50 each). For the intervention group, 10 two-hour virtual sessions of training family-centered problem-solving protocols were held. Post-test was performed on both groups. Data were analyzed in SPSS software (version 20) using frequency, mean and standard deviation, and analysis of covariance. Results: The findings showed that family-centered problem-solving training was effective on marital conflict, anxiety, and fear in nurses, and a significant difference was observed between the control and intervention groups (P&lt;0.001). Conclusion: Family-centered problem-solving training was effective on the negative effects of social distancing caused by COVID-19 in nurses' families.

A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets.

The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.

Effectiveness of spirituality-based problem solving on moral responsibility, and spiritual education of students

ABSTRACT This study purposed to the effectiveness of spirituality-based problem solving on Moral Responsibility)MR), and Spiritual Education (SE) of students. This study is practical in terms of purpose and semi-experimental in terms of the method with control and experimental groups. The survey sample consisted of two (test and control) groups of 25 people selected by the multi-stage cluster sampling method. The problem-solving education protocol was executed for the test group based on the model of Boalhari and Mohseni Kabir in seven 60-minute sessions. Data gathering tools included a) Sajedi Questionnaire and b) Carol spiritual education questionnaire. Results of the covariance analysis showed that problem solving based on spirituality have a positive and meaningful impact on the spiritual education (recognition, 0.63 behaviour, 0.53 and emotional 0.56) components of students, the findings also indicate that spirituality-based problem solving have a positive and meaningful impact on the students’ moral responsibility (civil, 0.45 and criminal, 0.36).

Similarity measure of agents’ ontologies in cohesive hybrid intelligent multi-agent system

The work is devoted to the issues of building cohesive hybrid intelligent multi-agent systems that implement the following mechanisms for ensuring the joint work of heterogeneous agents: coordination of goals and agent ontologies, development of the problem solving protocol. These mechanisms are similar to the processes that occur in long-standing expert teams. In them, experts, interacting in the course of solving problems, exchange views, knowledge, take into account each other’s goals, establish formal and informal norms of communication. In the paper, in particular, a method for estimating the similarity of ontologies is considered, which is the part of the method for estimating the cohesion of agents of the hybrid intelligent multi-agent system. The estimation of cohesion is used by system’s agents as the interaction effectiveness criterion when making decisions about the need to converge goals, ontologies or correction of the interaction protocol.

CpG-island-based annotation and analysis of human housekeeping genes

By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CpG islands (CGIs), their methylation states, CpG spacing and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be explained at individual gene and gene-family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI) and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA box on HK genes, remains to be elucidated. First, this study introduces such a problem-solving protocol for human-genome annotation, which is based on a combination of GTEx, JBLA and Gene Ontology (GO) analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; the HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic based on GO enrichment analysis. Finally, we demonstrate that Hadoop MapReduce-based MR-JBLA algorithm is more efficient than the original JBLA in k-mer counting and CGI-associated gene analysis.

Design thinking in online communities: suggestions for overcoming crowdsourcing issues

This paper is positioned at the intersection of two emerging trends in business: 'design thinking' (DT) and 'crowdsourcing'. DT is a new problem-solving protocol that is commonly used in offline environment but is increasingly being applied to online networks in an effort to tap into online crowds. This paper argues that the application of DT can solve various issues that managers grapple with in crowdsourcing. We further posit that network and consumer behaviour theories can be used to create contributor profiles that include information on the structure of relationships. This relationship information is suggested to help other participants and facilitators better evaluate and manage the contributions of crowd members.

Investigating the Effect of Complexity Factors in Stoichiometry Problems Using Logistic Regression and Eye Tracking

This paper includes two experiments, one investigating complexity factors in stoichiometry word problems, and the other identifying students' problem-solving protocols by using eye-tracking technology. The word problems used in this study had five different complexity factors, which were randomly assigned by a Web-based tool that we developed. The logistic regression analysis in the first experiment of this study showed that the ability of a student to achieve a correct answer was dependent on three complexity factors: number format, unit, and chemical equation. This was followed by an eye-tracking experiment, which reaffirmed that eye fixation durations were different between students at different levels of success when they solved chemistry word problems. The online tool in this study provides a general instrument to design chemistry word problems with various complexities, as well as a method to quantitatively assess problem difficulty and students' cognitive load. This can be applied in constructing curricula and examinations with desired difficulties in chemistry courses.

Curricular Reforms that Improve Students’ Attitudes and Problem-Solving Performance

We present the most recent steps undertaken to reform the introductory algebra-based course at The George Washington University. The reform sought to help students improve their problem-solving performance. Our pedagogy relies on didactic constructs such as the GW-ACCESS problem-solving protocol , instructional sequences and problem classification schemes that we have developed and implemented in our introductory physics course. These tools were designed to help advance students in two specific ways: 1) to improve their problem-solving performance, and 2) to improve their attitudes towards learning physics. We organized traditional and research-based physics problems such that students experienced a gradual increase in complexity related to problem context, problem features and cognitive processes needed to solve the problem. The instructional environment that we created is easily adaptable to any kind of curriculum and can be readily adjusted throughout the semester. To assess the students’ problem-solving performance, we created rubrics that assess key steps of physics problem solving. The Colorado Learning Attitudes about Science Survey (CLASS) was administered pre- and post-instruction to determine students’ shift in dispositions towards learning physics. The results show improvements in students’ problem-solving performance and in their attitudes towards learning physics.

Applying design thinking to biotechnology

I was recently invited to assemble a panel on applying design thinking to biotechnology for the conference, ‘A Better World by Design’, hosted by Brown University and the Rhode Island School of Design. This event was highly educational for me (and hopefully the other participants as well). Although many have been claiming that the business models used by biotechnology companies are flawed, and that the very structure of the biotechnology industry is likewise dysfunctional, there is a dearth of practical solutions. I am intrigued by the potential of applying design thinking to biotechnology, because it represents the introduction of a structured framework with a record of success in diverse areas. As a potential improvement to the often-narrow solutions directed by focused industry insiders, design-based solutions are often based on broad analysis and use a wide variety of interventions. Design thinking is a problem-solving protocol that emphasizes a deep definition of problems and open process to find and refine solutions. The basic elements of the design process are a thorough investigation of the problem (for example, the Toyota system of asking ‘why’ five times to find the roots of an observed problem) followed by the creation of many possible options. These options are then refined and potentially reiteratively reconsidered until potential solutions emerge. This process is an alternative to a simple focus on addressing observed bottlenecks (for example, funding gaps or clinical trial transitions). Design thinking has been used to improve the design of many consumer goods, and in scientific disciplines it has been applied to improve the ergonomics of research tools, and even the bottles containing cell culture media. 1 , 2 However, my primary interest is the application to non-engineering areas such as the methodologies of discovery-stage research (for example, can design thinking improve on the scientific method?), development-stage activities such as lead optimization and clinical trials, and commercial-stage activities such as reimbursement and regulatory compliance. This issue of the Journal of Commercial Biotechnology presents brief commentaries from two panelists at the design conference, Patrick Nef from the Malaria Medicines Venture and Art Boni from Carnegie Mellon University, who describe the use of design thinking in their own work. I have also begun soliciting papers from groups with extensive expertise in the application of design thinking to biotechnology, and look forward to sharing their perspectives in future issues. References REFERENCES AND NOTES Life Technologies Corporation. (2010) Invitrogen earns AmeriStar award for GIBCO cell culture bottle design, http://www.lifetechnologies.com/news-gallery/press-releases/2009/invitrogen-earns-ameristar-award-gibco-cell-culture-bottle-design.html , accessed October 2010. Life Technologies Corporation. (2010) The new GIBCO® bottle, http://www.invitrogen.com/site/us/en/home/Products-and-Services/Applications/Cell-Culture/CC-Misc/gibcoevolution.html , accessed October 2010.

Uso de representaciones y generalización de la regla del producto

ResumenAnalizamos los protocolos de resolución de problemas de 50 estudiantes con el objetivo de profundizar en la construcción de la regla del producto como esquema básico de resolución de problemas de conteo. En particular, indagamos en el uso que se hace de la inducción y de diferentes representaciones para pasar de la enumeración exhaustiva y el recuento total de posibilidades a la generalización de dicha regla. El análisis ha puesto de manifiesto la existencia de dos procesos de generalización: sobre la dimensión del problema y sobre el número de elementos que intervienen en cada factor. Mostramos cómo ambos procesos se relacionan con el uso efectivo de los diagramas de árbol que los estudiantes generan de manera espontánea y apuntamos posibles implicaciones para la instrucción. Por otra parte, el análisis de los datos ha generado la necesidad de indagar en la conexión entre las representaciones textuales y otros tipos de representaciones, evaluando su funcionalidad.

Substituting one mystery for another: the role of self-explanations in solving algebra word-problems

Explanations students provide themselves (self-explanations) in the course of learning or problem-solving have been shown to be positively associated with performance. However, the role self-explanation plays in problem solving has not been fully elaborated. This study aims to analyze the role of self-explanation in solving algebra word problems. We argue that self-explanation may function as verbal mediation that supports the transformation between different external representations of the problem. Our analysis of the problem solving protocols aims to illustrate this point through a multiple case studies design. Specifically we illustrate the way a particular kind of self-explanation (categorical explanation) mediates students' transformation from the sentential representation of the problem to the tabular one.

A theory-driven approach to assessing the cognitive effects of PBL

Medical schools are increasingly looking to case-based formats such as problem- based learning (PBL) for their medical students. However, the effects of PBL have not been adequately assessed for an informed decision. An approach to assessment should consider not just the knowledge outcomes expected of all students, but should be tailored to the theoretical goals of PBL: clinical reasoning, integration of scientific and clinical knowledge, and lifelong learning skills. This means that problem-solving processes as well as products need to be measured. In addition, cognitive measures associated with expert performance can be used to assess the extent to which PBL affects the development of expertise. In this study, students taking an elective in PBL were compared with students taking other electives on a realistic pathophysiological explanation task. The problem-solving protocols were examined for coherence, use of science concepts, strategy use, and self-directed learning. The results indicate that cognitive measures can be used to distinguish students who have participated in PBL from their counterparts in terms of knowledge, reasoning, and learning strategies. This suggests that such measures may play a meaningful role in assessment of student learning.

Impact of Superficial Similarity in the Application Phase of Analogical Problem Solving

Two groups ( n = 28, 30) of subjects solved a target problem after having learned either a superficially similar or a superficially dissimilar source. Structural similarity was kept constant. The problem materials were semantically rich short stories. Under both conditions, an equal number of subjects solved the target problem. However, the similar source group produced more carefully elaborated solutions, shorter problem-solving protocols containing more references to the source and less to the target, and a different amount and kind of mapping statements than the dissimilar source group. Only adaptation performance did not differ. Results support the existence of surface similarity impacts in the analogy application phase. Relationships to findings from the domain of mathematical analogies as well as theoretical implications are discussed.

Generative Activity During Geometry Problem Solving: Comparison of the Performance of High-Achieving and Low-Achieving High School Students

The problem-solving performance of groups of high-achieving (HA) and low-achieving (LA) Year 11 students was compared during solution of geometry problems using a think-aloud procedure. Students also undertook knowledge assessment tasks involving theorem recall and a prompted recall task to provide estimates of knowledge available for access during the solution attempts. Detailed analysis of problem-solving protocols indicated that HA students not only accessed a greater body of geometrical knowledge but also used that knowledge more effectively. On the more difficult problems, these HA students generated more information, used that generated information to access further relevant knowledge, and showed more frequent management of their processing behavior. The nature of the difference in generative activity of a student from each of the two groups is illustrated.

Problem‐Solving Processes of Pre‐Service Chemistry Teachers in Nigeria

Abstract This study investigated the problem solving difficulties of Pre‐service Chemistry teachers. A sample of 300 students in three Colleges of Education in Bendel State, Nigeria, participated in the study. The research instruments employed were three written tests and an interview. The analyses of the students’ responses to the written tests and problem solving protocols revealed that: (i) most of the students were capable of constructing appropriate mental representations of the problems but had difficulties in constructing solution plans; (ii) most of them had inappropriate problem‐solving approaches and limited knowledge of problem‐ solving strategies. They relied on the use of random trial and error and algorithms; (Hi) most of them exhibited structural errors relating to a disregard of given information and generation of necessary information; a misapplication of given information and a generation of wrong information in solving the problems and (iv) most of them failed to evaluate their problem‐...

Exploring the Episodic Structure of Algebra Story Problem Solving

This article analyzes the quantitative and situational structure of algebra story problems, uses these materials to propose an interpretive framework for written problem-solving protocols, and then presents an exploratory study of the episodic structure of algebra story problem solving in a sizable group of mathematically competent undergraduates. Analyses of written protocols compare the strategic, tactical, and conceptual content of solution attempts, looking within these attempts at the interplay between problem comprehension and solution. Comprehension and solution of algebra story problems are complementary activities, giving rise to a succession of problem-solving episodes. Although direct algebraic problem solving is sometimes effective, results suggest that the algebraic formalism may be of little help in comprehending the quantitative constraints posed in a problem text. Instead, competent problem solvers often reason within the situational context presented by a story problem, using various forms of model-based reasoning to identify, pursue, and verify quantitative constraints required for solution. The article concludes by discussing the implications of these finding for acquiring mathematical concepts (e.g., related linear functions) and for supporting their acquisition through instruction.

A case study: acquiring strategic knowledge for expert system development

The authors focus on methodological issues that establish knowledge engineering as an alternative to structured analysis and design, concentrating on the acquisition of strategic knowledge as an implementation structure determinant. They analyze benefits derived from applying expert systems in cellular telecommunications systems, discuss related artificial intelligence work, define structural characteristics of knowledge-based systems, examine a verbal problem-solving protocol from an expert, describe a prototypical knowledge-based program, and review methods and results. They discuss the role of deep knowledge in maintenance tasks, present the resulting system design, and examine the relevant development methodology. >

A case study: acquiring strategic knowledge for expert system development

The authors focus on methodological issues that establish knowledge engineering as an alternative to structured analysis and design, concentrating on the acquisition of strategic knowledge as an implementation structure determinant. They analyze benefits derived from applying expert systems in cellular telecommunications systems, discuss related artificial intelligence work, define structural characteristics of knowledge-based systems, examine a verbal problem-solving protocol from an expert, describe a prototypical knowledge-based program, and review methods and results. They discuss the role of deep knowledge in maintenance tasks, present the resulting system design, and examine the relevant development methodology.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Qualitative Modeling of Economic Systems

Models are at the heart of our ability to reason about complex systems. The content of our reasoning reflects the elements and relationships included in our system models. Analyses of problem solving protocols indicate that experts make use of several distinct types of models. Qualitative models represent a type of model that is designed to support reasoning about change. This reasoning process takes the form of a simulation that generates abstract summaries of possible system behaviors. Most research to date has explored the applicability of qualitative models to the representation and simulation of mechanical and electronic systems. In this paper, we review basic notions of qualitative modeling and suggest their straightforward application to economic reasoning . In addition, we introduce concepts sufficient to rep resent the equilibrium-based framework of many economic theories and to produce t he associated style of reasoning