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The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment.

Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can solve a mathematics problem correctly based on how well they solved other problems in the past. The usefulness of the model was evaluated by comparing the predicted probability of correct problem solving to the actual problem solving performance on the data set that was not used in the model building process.

The regularized logistic regression model showed a better predictive power than the standard Bayesian Knowledge Tracing model, the most frequently used quantitative model of student learning in the Educational Data Mining research.

Providing instructional scaffolding is critical in order to facilitate student learning. However, most computer-based learning environments use heuristics or rely on the discretion of students when they determine whether instructional scaffolding needs be provided. The predictive model of problem solving performance of students can be used as a quantitative guideline that can help make a better decision on when to provide instructional supports and guidance in the computer-based learning environment, which can potentially maximize the learning outcome of students.

This study aims to explore the role of planned, sudden shifts in lived experiences, in influencing learner capabilities towards improved problem-solving for sustainable development outcomes. The authors responded to employers of engineering and built environment graduates observing limited “real-life” problem-solving skills, beyond using established formulae and methods, in spite of attempts over more than two decades, to train engineers and other built environment disciplines in areas such as whole system design and sustainable design.

A grounded theory approach was used to guide the analysis of data collected through ethnographic methods. The process involved reflecting on authors’ efforts to develop context appreciation within a course called “International Engineering Practice”, using two years of collected data (archived course information, including course profile; completed assessment; lecture and field visit evaluations; and focus groups). The study is built on the authors’ working knowledge of Bloom’s Taxonomy and Threshold Learning Theory, and the well-established role of “context appreciation” in complex problem-solving. After the first iteration of the course, the authors looked for additional theoretical support to help explain findings. The Cynefin framework was subsequently used to augment the authors’ appreciation of “context” – beyond physical context to include relational context, and to evaluate students’ competency development across the four domains of “clear”, “complicated”, “complex” and “chaotic”.

This study helped the authors to understand that there was increased capacity of the students to distinguish between three important contexts for problem-solving, including an increased awareness about the importance of factual and relevant information, increased acknowledgement of the varying roles of professional practitioners in problem-solving depending on the type of problem and increased appreciation of the importance of interdisciplinary teams in tackling complex and complicated problems. There were several opportunities for such courses to be more effective in preparing students for dealing with “chaotic” situations that are prevalent in addressing the United Nations’ 17 sustainable development goals (UNSDGs). Drawing on the course-based learnings, the authors present a “context integration model” for developing problem-solving knowledge and skills.

The research findings are important because context appreciation – including both physical context and relational context – is critical to problem-solving for the UNSDGs, including its 169 targets and 232 indicators. The research findings highlight the opportunity for the Cynefin framework to inform holistic curriculum renewal processes, enhancing an educator’s ability to design, implement and evaluate coursework that develops physical and relational context appreciation.

The study’s findings and context integration model can help educators develop the full range of necessary problem-solving graduate competencies, including for chaotic situations involving high degrees of uncertainty. Looking ahead, acknowledging the significant carbon footprint of global travel, the authors are interested in applying the model to a domestic and/or online format of the same course, to attempt similar learning outcomes.

Connecting Bloom’s taxonomy deep learning and threshold learning theory critical path learning insights with the Cynefin framework context domains, provides a novel model to evaluate competency development for problem-solving towards improved holistic physical and relational “context appreciation” outcomes.

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

Problem solving skills (PSS), an important component of learning outcomes, is one of the desirable skills in engineering graduates as stated by many employers, researchers and government bodies in India for a strong foothold in professional world. There is a need to develop comprehensive understanding and integration of theory (concept) and practice (process) of PSS in the context of experiential learning (EL).

The present study is qualitative in nature using a conceptual research design focussing on synthesis and model building framework. The key elements of the study are PSS, EL and their integration. The study seeks to develop conceptual integration of PSS across multiple theories and perspectives. It offers an enhanced view of a concept of PSS by summarising and integrating extant knowledge. It presents the complete and comprehensive meaning/definition of PSS. Subsequently, it also explores EL and synthesises the different variants of EL that can be used to develop PSS. Finally, the study builds a theoretical framework that proposes integration and interplay between PSS and EL.

Problem-solving operates at three levels: problem concept (nature and context), process (stages with strategies) and solution (open-ended). EL can be used as a tool to develop PSS in an integrated manner. It is found that EL and problem-solving interplay with each other as both are cyclic in nature and have commonalities strengthening each other.

The proposed framework can be adopted in engineering education for making the engineering graduates job ready.

The study proposes a framework based on integration of EL and problem-solving focusing on specific aims and goals of the course, learning approaches, learning strategies and authentic learning (learning environment). This integration would bridge the gap between engineering education and industry requirements. EL integrated problem-solving focus on pedagogical knowledge (knowing how to facilitate discussion among learners and curricular knowledge) and instructional knowledge (knowing how to introduce, organise different methods and assess).

The paper's purpose is to promote the use of modern technologies such as multimedia packages to engineering students. The aim is to help them to learning in their learning, visualization, problem solving and understanding engineering concepts such as in mechanics dynamics.

TAPS packages are developed to help students solve selected engineering problems in a step‐by‐step approach. A comparison is made with existing commercial engineering packages to see if TAPS packages could further enhance the learning process.

The differences found were indicative of better presentation and clarity, step‐by‐step approach to solve engineering problems, user‐friendly environment, unbiased assessment of performance and flexibility to incorporate 3‐D geometric models in the TAPS packages.

The TAPS packages were compared with two commercial engineering packages and were based on a small number of users. A larger sample size of students would be required to see if TAPS packages are productive enough to be used locally in Malaysian universities and higher learning institutions.

The main originality of the paper can be seen from the development of the TAPS packages and the comparative study with existing commercial engineering packages. The differences found are explained in details in this paper.

Team cognition research continues to evolve as the need for understanding and improving complex problem solving itself grows. Complex problem solving requires members to engage in a number of complicated collaborative processes to generate solutions. This chapter illustrates how the Macrocognition in Teams model, developed to guide research on these processes, can be utilized to propose how intelligent tutoring systems (ITSs) could be developed to train collaborative problem solving. Metacognitive prompting, based upon macrocognitive processes, was offered as an intervention to scaffold learning these complex processes. Our objective is to provide a theoretically grounded approach for linking intelligent tutoring research and development with team cognition. In this way, team members are more likely to learn how to identify and integrate relevant knowledge, as well as plan, monitor, and reflect on their problem-solving performance as it evolves. We argue that ITSs that utilize metacognitive prompting that promotes team planning during the preparation stage, team knowledge building during the execution stage, and team reflexivity and team knowledge sharing interventions during the reflection stage can improve collaborative problem solving.

This paper aims to present the development of technology‐assisted problem solving (TAPS) packages at University Tenaga Nasional (UNITEN). The project is the further work of the development of interactive multimedia based packages targeted for students having problems in understanding the subject of engineering mechanics dynamics.

In this study TAPS packages are compared with other selected engineering computer packages.

The differences found were indicative of better presentation and clarity, step‐by‐step approach to solve engineering problems, user‐friendly environment, unbiased assessment of performance and flexibility to incorporate 3‐D geometric models in the TAPS packages.

This paper provides a brief account of the differences between the TAPS packages approach used in this research with that of commercial simulation packages accompanying the engineering mechanics dynamics textbook and will be of interest to those in the field of engineering.

The purpose of this paper is to provide empirical evidence of engineering knowledge creation in the context of product failure management, thereby extending knowledge about organizational learning and mindfulness to a largely unexplored context. The study addresses a gap in the literature by illustrating “engineering epistemology” as a critical knowledge asset that gives rise to superior problem solving – and potentially – superior business performance.

The authors conducted qualitative research based on phenomenological interviews with product engineers to generate a grounded theory about organizational knowledge creation. Rigorous analysis of narratives detailing the “lived lives” of problem solvers relied on a research protocol recommended by Corbin and Strauss.

The findings show that engineers' real‐world problem‐solving practices mirror Nonaka and Takeuchi's five phases of knowledge creation and the three stages of sensemaking in enactment theory, the genesis of Weick's notion of mindfulness. A synthesized model illustrates how a five‐step problem‐solving process facilitated by environmental conditions resulting in organizational learning is influenced by an “engineering epistemology”.

The sample was limited to engineers based primarily in the US Midwest. While the authors' methodology (grounded theory) was appropriate for theory generation, the results invite quantitative testing involving a larger and more diversified sample of engineers.

The paper highlights the social aspects of engineering problem solving that firms can optimize for effective problem investigation and higher organizational learning.

The paper conceptualizes problem‐solving teamwork as epistemic collaboration, with the often un‐optimized potential of generating organizational learning. It is, to the authors' knowledge, the first research to concentrate on modeling the dynamics of knowledge creation in an engineering problem‐solving context.

This paper aims at simulating on how “disorganization” affects team problem solving. The prime objective is to determine how team problem solving varies between an organized and disorganized environment also considering motivational aspects.

Using agent-based modeling, the authors use a real-world data set from 226 volunteers at five different types of non-profit organizations in Southwest England to define some attributes of the agents. The authors introduce the concepts of natural, structural and functional disorganization while operationalizing natural and functional disorganization.

The simulations show that “disorganization” is more conducive for problem solving efficiency than “organization” given enough flexibility (range) to search and acquire resources. The findings further demonstrate that teams with resources above their hierarchical level (access to better quality resources) tend to perform better than teams that have only limited access to resources.

The nuanced categories of “(dis-)organization” allow us to compare between various structural limitations, thus generating insights for improving the way managers structure teams for better problem solving.

This paper discusses methodology and technology to aid students learning programming. We have identified and integrated the problem solving and program development skills and knowledge students need to apply when programming with the cognitive activities required to accomplish these tasks. We then developed a composite methodological/software environment that supports the overall process of programming in a manner that gives appropriate weight to both language issues and problem solving. The results of a classroom evaluation of the method and the tool are then presented.