Improving Computer-Assisted Language Learning through Hierarchical Knowledge Structures

A common drawback in traditional language education is that all students in the same class use the same content. Since students may have different backgrounds such as prior knowledge and learning speed, one single curriculum may not be able to accommodate every student. Unfortunately, most students cannot afford personalized language learning, since preparing personalized learning content can be very time-consuming and potentially requires a significant amount of expert labor. Recently, researchers have proposed automatic systems to assist language education, such as Computer-based Assessment Systems (CAT) and Intelligent Tutoring Systems (ITS). However, previous work usually characterizes the student's knowledge and the difficulty of learning content using numeric scores, which may not be comprehensive. To improve on this, this thesis introduces hierarchical knowledge structures to assist in multiple tasks in language education. First, this structure multidimensionally characterizes the difficulty of each learning material by its relative difficulty to other materials and models the whole corpus with a graph structure. Additionally, we can utilize the hierarchical knowledge structure to multidimensionally assess a student's prior knowledge, predict the student's future performance on a specific task, and recommend learning content that is appropriate for each student. Furthermore, the hierarchical knowledge structure enables us to build a framework to characterize existing learning curricula extracted from textbooks and online learning tools, and apply expert wisdom that we have discovered to automatically design learning curricula. The hierarchical knowledge structure reduces the cost of expert labor and potentially makes language education more affordable and more engaging

Developing Learning Progressions in Support of the New Science Standards: A RAPID Workshop Series

The hypothetical learning progressions presented here are the products of the deliberations of two working groups of science education researchers, each group also including a state science curriculum supervisor, organized by the Consortium for Policy Research in Education (CPRE),with support from the National Science Foundation. Their charge was to produce hypothetical learning progressions describing the pathways students might be expected to follow as they acquire deep understanding of two of the core learning goals set by the National Research Council’s (NRC) Committee on a Conceptual Framework for the New K-12 Science Education Standards. The goals in question address students’ understanding of the structure, properties, and transformations of matter in the physical sciences and of the flow of matter and energy in ecosystems in the life sciences. These two core goals were chosen because a good bit of research has been done on children’s learning in these areas, some of it carried out by members of our working groups. These hypothetical learning progressions are intended to inform those who are working on the new national science standards, to serve as tools for those charged with developing curriculum and assessments to implement the new standards, and to encourage others to undertake the theoretical and empirical work needed to fill important gaps in our knowledge about learning progressions

Integrating the Molecular Basis of Sustainability into General Chemistry through Systems Thinking

The flow of materials and energy through society is an integral but poorly visible element of global sustainability agendas such as the Planetary Boundaries Framework and the UN Sustainable Development Goals (UNSDG). Given that the primary activities of chemistry are to analyze, synthesize, and transform matter, the practice of chemistry has a great deal to contribute to sustainability science, which in turn should play an increasingly important role in reshaping the practice of chemistry. Success in integrating sustainability considerations into the practice of chemistry implies a substantial role for chemistry education to better equip students to address the sustainability of earth and societal systems. Building on the framework of the IUPAC Systems Thinking in Chemistry Education (STICE) project, we develop approaches to using systems thinking to educate students about the molecular basis of sustainability, to assist chemistry to contribute meaningfully and visibly toward the attainment of global sustainability agendas. A detailed exemplar shows how ubiquitous coverage in general chemistry courses of the Haber–Bosch process for the synthesis of ammonia could be extended using systems thinking to consider the complex interplay of this industrial process with scientific, societal, and environmental systems. Systems thinking tools such as systems thinking concept map extension (SOCME) visualizations assist in highlighting inputs, outputs, and societal consequences of this large-scale industrial process, including both intended and unintended alterations to the planetary cycle of nitrogenous compounds. Strategies for using systems thinking in chemistry education and addressing the challenges its use may bring to educators and students are discussed, and suggestions are offered for general chemistry instructors using systems thinking to educate about the molecular basis of sustainability

A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas

Details the NRC's framework for new standards in K-12 science education that provide all students with some appreciation of science; knowledge to engage with scientific and technological information beyond school; and skills for careers of their choice

Scaffolding learning by modelling: The effects of partially worked-out models

Creating executable computer models is a potentially powerful approach to science learning. Learning by modelling is also challenging because students can easily get overwhelmed by the inherent complexities of the task. This study investigated whether offering partially worked-out models can facilitate students’ modelling practices and promote learning. Partially worked-out models were expected to aid model construction by revealing the overall structure of the model, and thus enabling student to create better models and learn from the experience. This assumption was tested in high school biology classes where students modelled the human glucose-insulin regulatory system. Students either received support in the form of a partial model that outlined the basic structure of the glucose-insulin system (PM condition; n = 26), an extended partial model that also contained a set of variables students could use to complete the model (PM+ condition; n = 21), or no support (control condition; n = 23). Results showed a significant knowledge increase from pretest to posttest in all conditions. Consistent with expectations, knowledge gains were higher in the two partial model conditions than in the control condition. Students in both partial model conditions also ran their model more often to check its accuracy, and eventually built better models than students from the control condition. Comparison between the PM and PM+ conditions showed that more extensive support further increased knowledge acquisition, model quality, and model testing activities. Based on these findings, it was concluded that partial solutions can support learning by modelling, and that offering both a structure of a model and a list of variables yields the best result