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One approach to encourage productive study strategies is to incorporate preparatory quizzes (or pre-quizzes) in which students are required to submit answers to questions before the underlying material is covered in class. In the present study, students took an introductory mechanical engineering class that either included pre-quizzes (treatment group) or did not (control group). Students in the treatment group visited the online textbook more often and earlier in advance of deadlines, indicating better management of their study time--behaviors that have been shown to be productive study strategies. They also performed better in the course, indicating that techniques intended to prime productive study strategies can pay off. Finally, measures of productive learning strategies correlated with measures of course performance for both groups. These findings support the pretesting principle, which holds that students study more effectively and learn better when they take practice tests before a lesson.

We present a technique that examines handwritten equations from a student's solution to an engineering problem and from this estimates the correctness of the work. More specifically, we demonstrate that lexical properties of the equations correlate with the grade a human grader would assign. We characterize these properties with a set of features that include the number of occurrences of various classes of symbols and binary and tripartite sequences of them. Support vector machine (SVM) regression models trained with these features achieved a correlation of r = 0.433 (p< 0.001) on a combined set of six exam problems. Prior work suggests that the number of long pauses in the writing that occur as a student solves a problem correlates with correctness. We found that combining this pause feature with our lexical features produced more accurate predictions than using either type of feature alone. SVM regression models trained using an optimized subset of three lexical features and the pause feature achieved an average correlation with grade across the six problems of r = 0.503 (p< 0.001). These techniques are an important step toward creating systems that can automatically assess handwritten coursework.

The present work examines the accuracy of self-reports of study time for college students. In a 10-week Mechanical Engineering course, 99 college students accessed their textbook, homework solutions, graded work, and lecture slides via custom software that recorded objective measures of reading time. In addition, the students provided subjective judgments of the time they spent reading these materials. Comparisons between the objective and subjective measures reveal that students significantly overestimated time with the textbook, homework solutions, graded work, and lecture slides, with higher performing students overestimating to a lesser degree. The difference between objective and subjective judgments of study time correlated significantly and negatively with final course grade for the textbook (r = -0.31), homework solutions (r = -0.39), and lecture slides (r = -0.24), but not for graded work (r = -0.05). This study calls into question the utility of self-report data in studies of student study habits, and showcases the value of objective technology-based measures of such habits.

There is a long history of research efforts aimed at understanding the relationship between homework activity and academic achievement. While some self-report inventories involving homework activity have been useful for predicting academic performance, self-reported measures may be limited or even problematic. Here, we employ a novel method for accurately measuring students' homework activity using smartpen technology. Three cohorts of engineering students in an undergraduate statics course used smartpens to complete their homework problems, thus producing records of their work in the form of timestamped digitized pen strokes. Consistent with the time-on-task hypothesis, there was a strong and consistent positive correlation between course grade and time doing homework as measured by smartpen technology (r = 0.44), but not between course grade and self-reported time doing homework (r = -0.16). Consistent with an updated version of the time-on-task hypothesis, there was a strong correlation between measures of the quality of time spent on homework problems (such as the proportion of ink produced for homework within 24 hr of the deadline) and course grade (r = -0.32), and between writing activity (such as the total number of pen strokes on homework) and course grade (r = 0.49). Overall, smartpen technology allowed a fine-grained test of the idea that productive use of homework time is related to course grade.

We present a two-step technique for learning reusable design procedures from observations of a designer in action. This technique is intended for the domain of parametric design problems in which the designer iteratively adjusts the parameters of a design so as to satisfy the design requirements. In the first step of the two-step learning process, decision tree learning is used to infer rules that predict which design parameter the designer is likely to change for any particular state of an evolving design. In the second step, decision tree learning is again used, but this time to learn explicit termination conditions for the rules learned in the first step. The termination conditions are used to predict how large of a parameter change should be made when a rule is applied. The learned rules and termination conditions can be used to automatically solve new design problems with a minimum of human intervention. Experiments with this technique suggest that it can reproduce the decision making process observed from the designer, and it is considerably more efficient than the previous technique, which was incapable of learning explicit rule termination conditions. In particular, the rule termination conditions allow the new program to automatically solve design problems with far fewer iterations than previously required. [DOI: 10.1115/1.3066681]

Sketches are a ubiquitous form of communication in engineering design due to their simplicity and efficiency. However, because of a lack of suitable machine-interpretation techniques, they are virtually unusable with current computer-aided design and engineering tools. The informal nature of sketches and their inherent ambiguity present a number of challenges to the development of such techniques. Here we address one particular challenge, the task of reliably locating and recognizing the intended visual objects from a continuous stream of pen strokes. We present an integrated sketch parsing and recognition approach, based on a novel mark-group-recognize paradigm, which is tailored to the domain of mechanical systems. In the first step of processing, the sketch is examined to identify certain delimiting symbols called 'markers.' The remaining pen strokes are then partitioned into distinct clusters, each representing a single symbol. Finally, a trainable symbol recognizer is used to find the best interpretation of each cluster We have used these techniques to build a sketch-based tool for designing and analyzing vibratory mechanical systems. This tool enables designers to analyze and animate vibratory systems by simply sketching them on a tablet computer User studies indicate that even first-time users find our tool to be effective. [DOI: 10.1115/1.2965595] Keywords: sketch understanding, pen computing, sketch parsing, symbol recognition, vibratory mechanical systems

RedesignIT is a computer program that uses model-based reasoning to generate and evaluate proposals of redesign plans for engineered devices. These proposals describe how the design parameters could be changed to achieve a specified performance goal. Equally important, the program proposes complementary modifications that may be necessary to counteract the undesirable side effects of the primary changes. RedesignIT is intended for use during the first stages of a redesign project, when engineers need to make a quick, yet accurate assessment of the overall effects of a particular design change. The program uses qualitative device models, which allow it to compute redesign plans efficiently. With its ability to predict the collateral, and probably undesirable, effects of a design change, the program is well suited to aid product designers in deciding on the feasibility of introducing design changes to a product. [DOI: 10.1115/l.1666888]

The Electronic Cocktail Napkin (ECN) is a freehand drawing environment that functions as an interface for several design applications. It supports a digitizing table, a whiteboard and a personal digital assistant to permit several users to draw on different devices. It also features symbol recognition capabilities, parsing and spatial relationship identification features. The design applications supported by ECN include visual indexing, simulation interfacing and HTML editing.

Summary: One approach to encourage productive study strategies is to incorporate preparatory quizzes (or pre‐quizzes) in which students are required to submit answers to questions before the underlying material is covered in class. In the present study, students took an introductory mechanical engineering class that either included pre‐quizzes (treatment group) or did not (control group). Students in the treatment group visited the online textbook more often and earlier in advance of deadlines, indicating better management of their study time—behaviors that have been shown to be productive study strategies. They also performed better in the course, indicating that techniques intended to prime productive study strategies can pay off. Finally, measures of productive learning strategies correlated with measures of course performance for both groups. These findings support the pretesting principle, which holds that students study more effectively and learn better when they take practice tests before a lesson. [ABSTRACT FROM AUTHOR]

We describe LearnIT, a computer program that can observe an iterative solution to a parametric design problem and learn the design strategy employed. When the design requirements change, the program uses the learned strategy to automatically generate a new solution in the 'style' of the original. The program uses a specialized instance-based learning method based on the observation that iterative design is often a form of debugging--each iteration is an attempt to repair a particular flaw in the design. Thus, the program learns the design strategy by observing what actions are taken in response to each kind of flaw. [S1050-0472(00)01203-4]

We describe an approach for automatically computing a class of design rationales. Our focus is computing the purposes of the geometric features on the parts of a device. This is accomplished by first simulating the device with the feature in question removed and comparing this to a simulation of the nominal device. The differences between the simulations are indicative of the behaviors that the feature ultimately causes. Fundamental principles of mechanics are then used to construct a causal explanation that describes how the feature causes these behaviors. This explanation constitutes one of the rationales for the feature. We have implemented a computer program called ExplainIT that uses this approach to compute rationales and we have tested it on various examples. [S1050-0472(00)01001-1] Keywords: Design Rationale Capture, Interpreting Simulations, Causal Reasoning, Qualitative Reasoning