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Game‐based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game‐based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with the learning environment with conventional methods, such as multiple choice content knowledge assessments. While previous student modelling approaches have leveraged machine learning to automatically infer students' knowledge, there is limited work that incorporates the fine‐grained content from each question in these types of tests into student models that predict student performance at early junctures in gameplay episodes. This work investigates a predictive student modelling approach that leverages the natural language text of the post‐gameplay content knowledge questions and the text of the possible answer choices for early prediction of fine‐grained individual student performance in game‐based learning environments. With data from a study involving 66 undergraduate students from a large public university interacting with a game‐based learning environment for microbiology, Crystal Island, we investigate the accuracy and early prediction capacity of student models that use a combination of gameplay features extracted from student log files as well as distributed representations of post‐test content assessment questions. The results demonstrate that by incorporating knowledge about assessment questions, early prediction models are able to outperform competing baselines that only use student game trace data with no question‐related information. Furthermore, this approach achieves high generalisation, including predicting the performance of students on unseen questions. Practitioner notesWhat is already known about this topic A distinctive characteristic of game‐based learning environments is their capacity to enable fine‐grained student assessment.Adaptive game‐based learning environments offer individualisation based on specific student needs and should be able to assess student competencies using early prediction models of those competencies.Word embedding approaches from the field of natural language processing show great promise in the ability to encode semantic information that can be leveraged by predictive student models.What this paper adds Investigates word embeddings of assessment question content for reliable early prediction of student performance.Demonstrates the efficacy of distributed word embeddings of assessment questions when used by early prediction models compared to models that use either no assessment information or discrete representations of the questions.Demonstrates the efficacy and generalisability of word embeddings of assessment questions for predicting the performance of both new students on existing questions and existing students on new questions.Implications for practice and/or policy Word embeddings of assessment questions can enhance early prediction models of student knowledge, which can drive adaptive feedback to students who interact with game‐based learning environments.Practitioners should determine if new assessment questions will be developed for their game‐based learning environment, and if so, consider using our student modelling framework that incorporates early prediction models pretrained with existing student responses to previous assessment questions and is generalisable to the new assessment questions by leveraging distributed word embedding techniques.Researchers should consider the most appropriate way to encode the assessment questions in ways that early prediction models are able to infer relationships between the questions and gameplay behaviour to make accurate predictions of student competencies. [ABSTRACT FROM AUTHOR]

This exploratory paper highlights how problem‐based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from Crystal Island: EcoJourneys, a collaborative game‐based learning environment centred on supporting science inquiry. In Crystal Island: EcoJourneys, students work in teams of four, investigate the problem individually and then utilize a brainstorming board, an in‐game PBL whiteboard that structured the collaborative inquiry process. The paper addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game‐based learning environment? Drawing on a mixed method approach, we first analyzed students' pre‐ and post‐test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre‐ and post‐test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game‐based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game‐based learning environment: students engaged in low to moderate self‐directed actions with (1) high and (2) moderate collaborative sense‐making actions, (3) low self‐directed with low collaborative sense‐making actions and (4) high self‐directed actions with low collaborative sense‐making actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self‐directed and high collaborative sense‐making cluster actively initiated discussions and integrated information they learned to the problem, whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students' interactions with in‐game activities. Practitioner notesWhat is already known about this topicLearning analytic methods have been effective for understanding student learning interactions for the purposes of assessment, profiling student behaviour and the effectiveness of interventions.However, the interpretation of analytics from these diverse data sets are not always grounded in theory and challenges of interpreting student data are further compounded in collaborative inquiry settings, where students work in groups to solve a problem.What this paper addsProblem‐based learning as a pedagogical framework allowed for the design to focus on individual and collaborative actions in a game‐based learning environment and, in turn, informed the interpretation of game‐based analytics as it relates to student's self‐directed learning in their individual investigations and collaborative inquiry discussions.The combination of principal component analysis and qualitative interaction analysis was critical in understanding the nuances of student collaborative inquiry.Implications for practice and/or policySelf‐directed actions in individual investigations are critical steps to collaborative inquiry. However, students may need to be encouraged to engage in these actions.Clustering student data can inform which scaffolds can be delivered to support both self‐directed learning and collaborative inquiry interactions.All students can engage in knowledge‐integration discourse, but some students may need more direct support from teachers to achieve this. [ABSTRACT FROM AUTHOR]

Collaborative inquiry learning affords educators a context within which to support understanding of scientific practices, disciplinary core ideas, and crosscutting concepts. One approach to supporting collaborative science inquiry is through problem‐based learning (PBL). However, there are two key challenges in scaffolding collaborative inquiry learning in technology rich environments. First, it is unclear how we might understand the impact of scaffolds that address multiple functions (e.g., to support inquiry and argumentation). Second, scaffolds take different forms, further complicating how to coordinate the forms and functions of scaffolds to support effective collaborative inquiry. To address these issues, we identify two functions that needed to be scaffolded, the PBL inquiry cycle and accountable talk. We then designed predefined hard scaffolds and just‐in‐time soft scaffolds that target the regulation of collaborative inquiry processes and accountable talk. Drawing on a mixed method approach, we examine how middle school students from a rural school engaged with Crystal Island: EcoJourneys for two weeks (N=45). Findings indicate that hard scaffolds targeting the PBL inquiry process and soft scaffolds that targeted accountable talk fostered engagement in these processes. Although the one‐to‐one mapping between form and function generated positive results, additional soft scaffolds were also needed for effective engagement in collaborative inquiry and that these soft scaffolds were often contingent on hard scaffolds. Our findings have implications for how we might design the form of scaffolds across multiple functions in game‐based learning environments. [ABSTRACT FROM AUTHOR]

A distinctive feature of game‐based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor‐based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students' interactions with game‐based learning environments hold significant promise for developing a deeper understanding of game‐based learning, designing game‐based learning environments to detect maladaptive behaviors and informing adaptive scaffolding to support individualized learning. This paper introduces a multimodal learning analytics approach that incorporates student gameplay, eye tracking and facial expression data to predict student posttest performance and interest after interacting with a game‐based learning environment, Crystal Island. We investigated the degree to which separate and combined modalities (ie, gameplay, facial expressions of emotions and eye gaze) captured from students (n = 65) were predictive of student posttest performance and interest after interacting with Crystal Island. Results indicate that when predicting student posttest performance and interest, models utilizing multimodal data either perform equally well or outperform models utilizing unimodal data. We discuss the synergistic effects of combining modalities for predicting both student interest and posttest performance. The findings suggest that multimodal learning analytics can accurately predict students' posttest performance and interest during game‐based learning and hold significant potential for guiding real‐time adaptive scaffolding. [ABSTRACT FROM AUTHOR]

Because there are many potential risks in the MR environment and reports of adverse incidents involving patients, equipment and personnel, the need for a guidance document on MR safe practices emerged. Initially published in 2002, the ACR MR Safe Practices Guidelines established de facto industry standards for safe and responsible practices in clinical and research MR environments. As the MR industry changes the document is reviewed, modified and updated. The most recent version will reflect these changes. J. Magn. Reson. Imaging 2013;37:501-530. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Discusses the development of a descriptive model of program termination, based on literature on termination of policies, programs and organizations. Variables believed to affect program termination; Inherent characteristics; Political environment; Constraints; Application of the framework in a case study of revenue sharing; Termination of revenue sharing.

Introduces papers presented at a symposium on hazardous waste politics and policy, published in the September 1985 issue of the "Policy Studies Journal".

The article discusses about various issues related to public policy implementation. The major impediments obstructing the further development of the field of implementation research can be traced to three different, but related inadequacies, (1) theoretical pluralism, (2) restricted context and (3) a lack of cumulation. If researchers recognize the limitations of implementation research as it is currently practiced and consciously design research strategies which address design weaknesses, researchers would be able to develop and test richer and more differentiated theories of implementation.

The purpose of this article is to present three models of public policy formation as potential explanations for ocean, incineration policy. These models include the rational model of decision making, the pluralist model, and the elitist model. In the rational model, attention is given to the U.S.-based Environmental Protection Agency itself and the internal dynamics of that organization in fashioning ocean incineration policy. In the pluralist model, attention is focused on the role and influence of various interest groups as they attempt to affect policy outcomes. Finally, in the elitist model, the influence of the sea-based incineration industry upon ocean incineration policy is examined.

Deals with the impact of technology on international politics. Variables related to international organization and collaboration; Causal relationships among the components of international relations; World problems that need coordinated responses.

The article reviews four books on aspects of the raw materials problem. "The Future of Nonfuel Minerals," by John E. Tilton identifies the possible causes of mineral shortages, assesses the actual threat they pose for the future. "Commodity Market Controls: A Historical Review," by Carmine Nappi provides a history of commodity market control policy from World War I to 1977. "Deep Seabed Resources: Politics and Technology," by Jack N. Barkenbus examines the political dispute over manganese nodules in order to explore and explain why the formation of a governing regime for deep seabed resources has progressed so unsatisfactorily. Finally, "The World Copper Industry: Structure and Economic Analysis," by Raymond F. Mikesell focuses on the physical characteristics of the copper industry and its market and industrial structure as a means for understanding policy issues relating to copper.