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It is known that timely and personalized feedback is vital to the learning process, and because of increasing enroll- ment, instructors can find it harder to provide that feedback. Learning analytics presents a solution to this problem. The growth in popularity of online education systems better enables learning analytics by providing additional educational data. This work focuses on the analysis of students’ incorrect short answers and their pathways to correct solutions. By considering student submissions as sequences, this work uses a dimension called “distance” which can be used to predict how far off a student’s incorrect answer is from a correct one. This distance metric can be used for recognizing students who may need help, understanding which concepts students struggle with, evaluating assessment questions, and improving multiple-choice answers. This paper discusses the methods, relevant learning scenarios, and applications of the learning analytics system. It features the results and analysis of a usability test conducted on 56 faculty members. 

Understanding the thought processes of students as they progress from initial (incorrect) answers toward correct answers is a challenge for instructors, both in this pandemic and beyond. This paper presents a general network visualization learning analytics system that helps instructors to view a sequence of answers input by students in a way that makes student learning progressions apparent. The system allows instructors to study individual and group learning at various levels of granularity. The paper illustrates how the visualization system is employed to analyze student responses collected through an intervention. The intervention is BeginToReason, an online tool that helps students learn and use symbolic reasoning-reasoning about code behavior through abstract values instead of concrete inputs. The specific focus is analysis of tool-collected student responses as they perform reasoning activities on code involving conditional statements. Student learning is analyzed using the visualization system and a post-test. Visual analytics highlights include instances where students producing one set of incorrect answers initially perform better than a different set and instances where student thought processes do not cluster well. Post-test data analysis provides a measure of student ability to apply what they have learned and their holistic understanding. 

Greenhouses conserve land and water while increasing crop production, making them an attractive system for low environmental impact agriculture. Yet, to achieve this goal, there is a need to reduce their large energy demand. Employing semitransparent organic solar cells (OSCs) on greenhouse structures provide an opportunity to offset the greenhouse energy needs while maintaining the lighting needs of the plants. However, the design trade-off involved in optimizing solar power generation and crop productivity to maximize greenhouse economic value is yet to be studied in detail. Here, a functional plant growth model is integrated with a dynamic energy model that includes supplemental lighting to optimize the economics of growing lettuce and tomato. The greenhouse optimization considers 64 different OSC active layers with varying roof coverage for 25 distinct climates providing a global perspective. We find that crop yield is the primary economic driver, and that crop yield can be maintained in OSC-greenhouses across diverse climates. The crop productivity along with the energy produced by the OSCs results in improved net present value of the OSC-greenhouses relative to conventional systems in most climates for both lettuce and tomato. In addition, we find common solar cell active layers that maximize greenhouse economic value resulting in guidelines for scaling up OSC-greenhouse design. Through this model framework, we highlight the opportunity for OSCs in greenhouses, uncover designs and locations that provide the most value, and provide a basis for further development of OSC-greenhouses to achieve a sustainable means of food production. 

Online learning has become desirable for many students. In the U.S., more than one-third of all enrolled students participate in at least one online course [13]. The most effective online learning environments allow students to work at their own pace, from any location, at any time, and to receive automated feedback. In light of these benefits and the likely protracted impact of the current public health crisis, the trend toward online learning is likely to increase. 

To develop code that meets its specification and is verifiably correct, such as in a software engineering course, students must be able to understand formal contracts and annotate their code with assertions such as loop invariants. To assist in developing suitable instructor and automated tool interventions, this research aims to go beyond simple pre- and post-conditions and gain insight into student learning of loop invariants involving objects. As students develop suitable loop invariants for given code with the aid of an online system backed by a verification engine, each student attempt, either correct or incorrect, was collected and analyzed automatically, and catalogued using an iterative process to capture common difficulties. Students were also asked to explain their thought process in arriving at their answer for each submission. The collected explanations were analyzed manually and found to be useful to assess their level of understanding as well as to extract actionable information for instructors and automated tutoring systems. Qualitative conclusions include the impact of the medium. 

Object-based development using design-by-contract (DbC) is broadly taught and practiced. Students must be able to read and write symbolic DbC assertions that are sufficiently precise and be able to use these assertions to trace program code. This paper summarizes the results of using an automated tool to pinpoint fine-grain difficulties students face in learning to symbolically trace code involving objects. The pilots were conducted in an undergraduate software engineering course. Quantitative results show that data collected by the tool can help to identify and classify learning obstacles. Qualitative findings help validate student misunderstandings underlying these difficulties. Analysis of exam questions helps understand the persistence of student learning to read and write simple assertions about code behavior. Together, these results provide directions for intervention. 

Abstract Greenhouse vegetable production plays a vital role in providing year‐round fresh vegetables to global markets, achieving higher yields, and using less water than open‐field systems, but at the expense of increased energy demand. This study examines the life cycle environmental and economic impacts of integrating semitransparent organic photovoltaics (OPVs) into greenhouse designs. We employ life cycle assessment to analyze six environmental impacts associated with producing greenhouse‐grown tomatoes in a Solar PoweRed INtegrated Greenhouse (SPRING) compared to conventional greenhouses with and without an adjacent solar photovoltaic array, across three distinct locations. The SPRING design produces significant reductions in environmental impacts, particularly in regions with high solar insolation and electricity‐intensive energy demands. For example, in Arizona, global warming potential values for a conventional, adjacent PV and SPRING greenhouse are found to be 3.71, 2.38, and 2.36 kg CO₂eq/kg tomato, respectively. Compared to a conventional greenhouse, the SPRING design may increase life cycle environmental burdens in colder regions because the shading effect of OPV increases heating demands. Our analysis shows that SPRING designs must maintain crop yields at levels similar to conventional greenhouses in order to be economically competitive. Assuming consistent crop yields, uncertainty analysis shows average net present cost of production across Arizona to be $3.43, $3.38, and $3.64 per kg of tomato for the conventional, adjacent PV and SPRING system, respectively.