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The conceptual framework of mathematical modeling (e.g., Lesh & Doerr, 2003) is a vital area in mathematics education research, and its implementation has potential for deeply involving children in integrated and meaningful learning. In mathematical modeling learners are active agents in content-integrated, real-world problem solving. This emphasis on integrating multiple content areas to answer big questions, the pursuit of mathematical modeling, descends from Dewey’s work. We present the definition, principles, and design of modeling practices for readers who may be familiar with early childhood curriculum but less so with using modeling for learning. We explore the application of mathematical modeling to early childhood classrooms and its compatibility with early childhood pedagogies and philosophies. Young children may often be underestimated, assumed to be unable to pose big questions that can be answered through activity, experience, and data; but we discuss how young children can be engaged in problems through mathematical modeling. Finally, as preservice teacher educators, we discuss preparing preservice and in-service teachers for modeling in their classrooms. We offer examples and guidance for early childhood teachers to engage in authentic practice – meeting children where their interests are and creating integrated problem-solving experiences.

Interest is currently growing in open social learner modeling (OSLM), which means making peer models and a learner's own model visible to encourage users in e-learning. The purpose of this study is to examine students' views about the OSLM in an e-learning system.

This case study was conducted with 40 undergraduate students enrolled in advanced programming and database management system courses. A Likert-type questionnaire and open-ended questions were used to obtain the students' views. System usage data were also analyzed to ensure the richness and diversity of the overall data set.

The quantitative data of the students' views were analyzed with descriptive statistics; the results are presented as graphics. The qualitative data of the students' views were examined by content analysis to derive themes. These themes are organized into four subtopics: the students' positive views, their negative views, their improvement suggestions and their preferences about using similar OSLM visualizations in other e-learning systems. The students' subjective views are discussed in the context of their recorded interactions with the system.

Competition due to seeing peer models was considered by participants both as positive and negative features of the learning system. So, this study revealed that, the ways to combine peer learner models to e-learning systems that promote positive competition without resulting social pressure, still need to be explored.

By combining open learner models with open peer models, OSLM enhances the learning process in three different ways: it supports self-regulation, encourages competition and empowers self-evaluation. To take advantage of these positive contributions, practitioners should consider enhancing e-learning systems with both own learner and peer model features.

Despite increasing interest in OSLM studies, several limitations and problems must be addressed such as sparsity of data and lack of study of different contexts and cultures. To date, no published study in this area exists in Turkey. The purpose of this study is to fill this gap by examining OSLM features in an e-learning system from the perspectives of Turkish students by using both their system interaction data and their subjective views.

This chapter focuses on the state-of-the-art modeling approaches used in Intelligent Tutoring Systems (ITSs) and the frameworks for researching and operationalizing individual and group models of performance, knowledge, and interaction. We adapt several ITS methodologies to model team performance as well as individuals’ performance of the team members. We briefly describe the point processes proposed by von Davier and Halpin (2013), and we also introduce the Competency Architecture for Learning in teaMs (CALM) framework, an extension of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg, & Holden, 2012) to be used for team settings.

This study aims to propose and test a model that examines the potential connections between two teacher situational variables (teacher immediacy and credibility) and three learner affective factors (motivation, attitudes and communication confidence) and to examine how such associations predict learners’ L2WTC (Foreign/second language willingness to communicate) in a language class via a comprehensive communication model to structurally verify the theoretically based associations among these variables.

In total, 214 females and 198 males took part in the study with age range between 19 and 38 years. Participants filled in a verified, translated Arabic version of the questionnaires using an online questionnaire. Data were gathered using questionnaires and were analyzed using descriptive statistics, confirmatory factor analysis, path analysis and sequential mediation analysis using bootstrapping methods to identify and verify direct and indirect paths in the model.

The initial L2 communication structural model showed acceptable goodness of model fit. Teacher credibility and immediacy behaviors only indirectly predicted L2WTC through the mediation of affective variables. Motivation and communication confidence mediated the relationship between credibility and L2WTC, while the association between immediacy and L2WTC was mediated by communication confidence.

The findings of this study have important pedagogical implications globally for professions related to communication instruction, especially with regard to teacher credibility behaviors and particularly for practitioners and beneficiaries in EFL contexts where learners are widely acknowledged for their unwillingness to communicate in foreign language classes.

Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session, therefore, they need to be addressed in real time to keep the learner engaged in the learning process. Similarly, long-term learners’ motivation plays an equally important role to retain the learner in the long run and minimize the risk of dropout. Therefore, the purpose of this study is to incorporate a comprehensive learner motivation model that is based on short-term and long-term aspects of the learners' motivation. This approach enables Web-based educational systems to identify the real-time motivational state of the learner and provide personalized interventions to keep the learners engaged in learning process.

Recent research regarding personalized Web-based educational systems demonstrates learner’s motivation to be an essential component of the learning model. This is because of the fact that low motivation results in either students’ less engagement or complete drop out from the learning activities. A learner motivation model is considered to be a set of perceptions and beliefs that the system has developed about a learner. This includes both short-term and long-term motivations of leaners.

This study proposed a framework of a domain independent learners’ motivation model based on firm educational theories. The proposed framework consists of two modules. The primary module deals with real-time identification of motivation and logging off activities such as login, forum participation and adherence to assessment deadline. Secondary module maintains the profile of leaners associated with both short-term and long-term motivation. A study was conducted to verify the impact of learners’ motivation model and personalized interventional strategies based on proposed model, using Systematical Information Education Method assessment standards. The results show an increase in motivational index and the characteristics associated with motivation during the conducted study.

Motivational diagnosis is important for both traditional classrooms and Web-based education systems. It is one of the major elements that contribute in the success of the learning process. However, dropout rate among online students is very high, which leads to incorporate motivational elements in more personalized way because motivated students will retain the course until they successfully complete it. Hence, identifying learner’s motivation, updating learners’ motivation model based on this identification and providing personalized interventions are the key for the success of Web-based educational systems.

Fostering the development of professional character in student physicians remains the most essential, yet challenging and sometimes elusive goal of those in medical education. Current understandings and contemporary approaches to learning and teaching can provide perspectives that may inform our thinking. In this chapter, learning with and from others is explored along with approaches that form the foundation for the development of professional character that integrates moral conduct into professional practice. The implications for both teaching and learning and the importance of the learning environment are discussed. Education as a moral endeavor and values-based practice is emphasized.

The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods.

This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research.

After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments.

This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account.

The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

This chapter examines some of the challenges and emerging strategies for authoring, distributing, managing, and evaluating Intelligent Tutoring Systems (ITSs) to support computer-based adaptive instruction for teams of learners. Several concepts related to team tutoring are defined along with team processes, and fundamental tutoring concepts are provided including a description of the learning effect model (LEM), an exemplar describing interaction between learners and ITSs with the goal of realizing optimal tutor decisions. The challenges noted herein are closely related to the LEM and range from acquisition of learner data, synthesis of individual learner and team state models based on available data, and tutor decisions which center on optimizing strategies (recommendations) and tactics (actions) given the state of the learner, the team, and the conditions under which they are being instructed, the environment. Finally, we end this chapter with recommendations on how to use this book to understand and design effective ITSs for teams.

Grounded on components of experiential learning theory (ELT) and self-regulation of learning (SRL) theory, augmented by elements of action theory and script development, the purpose of this paper is to demonstrate the case-based modeling (CBM) instructional approach that stimulates learning in groups or teams. CBM is related to individual and group learning, reflective practices, and the growth mindset. The whole instructional approach is explained in detail as well as unintended consequences and benefits.

The important features of ELT and SRL are presented as well as details of the use of cases (case studies, scenarios, and stories), the creation of case material, and the dynamics of script development. A detailed presentation of the CBM approach with attendant explanation of how the various theories are connected to the steps of the approach is offered. Empirical evidence of the efficacy of CBM is expressed.

The CBM approach may be used effectively in collegiate instruction, employee training and problem solving (in groups), and in other settings. The outputs of the instruction and its processes should result in malleable performance (behavioral) scripts intended to improve both learning and performance. The approach requires high-level involvement and attention to detail.

The paper details the confluence of two important learning theories: experiential learning and SRL that result in a formal instructional model. Activities require learning on the individual and group level. The entire instructional model is presented in a way that a practitioner may implement the model. In addition, substantial detail is provided on the construction of case material and the sources of case material.

Course recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.

The study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.

The main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.

The findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.

This study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.