Stanford University Masters Computer Science – New research shows that being a tinkerer and being ready helps when learning computer programming. (Photo copyright: Shutterlink)
The College of Higher Education and the Department of Computer Science are engaged in joint research to develop new approaches to teaching.
Stanford University Masters Computer Science
After conducting an analysis of the accuracy of students’ actions while learning to write computer programs, researchers at Stanford University were able to determine—with incredible accuracy—the core final grade. What makes it the most effective is that the predictions are based on the learning standards identified by the students’ work at the beginning of the course.
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The work shows the potential for classrooms in the future to have teachers who can see how well their students are learning—while the information is being taught—and then correct them. lessons in real time to meet the needs of their students.
Is the result of collaborative research between Stanford’s Computer Science Department and its Graduate School of Education. Four professors and two students collected more than 154,000 snapshots of average coding efforts from 370 students enrolled in a graduate program in education over the summer. and the fall of 2012.
“The way we measure students often has one big problem – it’s just looking at students before they learn and after they learn,” said the paper’s director Paulo Blickstein, the assistant professor of education. “Now, for the first time, we have the technology that will enable systematic assessment – measuring people continuously as they learn.”
In addition to Blickstein, other authors are Marcelo Worsley, a former student; Chris Piech, current computer science student; Mehran Sahami, professor and associate director for teaching in the Department of Computer Science; Steven Cooper, associate professor in the Department of Computer Science; and Daphne Koller, the Rajeev Motwani Professor in the Department of Computer Science.
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The paper presents findings from two studies, both of which used computer techniques, commonly known as “machine learning,” to analyze data collected each time a child learn to write or write a program.
The first study looked for patterns of revision across a range of subjects, trying to relate these patterns to final test scores.
Some of the findings are surprising: for example, the authors found no relationship between the “tinkering” – often thought to mean lack of knowledge of the work – and the work of course. However, the authors found that students who changed their programming profession—going from “programmer” to “engineer” or other—were the ones who did best. thing, said.
The second study explored a project in depth, trying to explore different opportunities by creating a detailed “success map” of student work that can link to class work.
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Using these results, the researchers have identified a number of activities among 370 students. Among these, the three patterns identified as ‘alpha’, ‘beta’ and ‘gamma’ are the most common. . By dividing students into these subjects, researchers can determine their final test scores with more accuracy than simply adding up their average scores.
Alpha students, writer Piech explains, move from one to another well as they write numbers, creating a challenge. Beta and Gamma students, on the other hand, are enrolled in what is known as a “failed state,” where their programs affect the end they must leave before they can again complete.
Discovering these “watershed states” and how students get into them has more potential than grading: they open the door to creating a process to encourage students to follow the good results before they fall in the work.
The document includes an important warning. Although many students fit neatly into these three categories, the researchers were surprised by how many other good ways they found to learn programming. “We see that students have different backgrounds, styles and education. Although not all groups are equal, many methods are successful in the task, “Blickstein said. “The findings suggest that we should be more accepting of their differences. instead of trying to be a model for everyone in one way.”
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Sahami, the author of the study that introduced Stanford’s introductory program, called the study unusual for collaboration between experts in education and computer science. “I’ve been involved in computer science for a long time, and also in machine learning and data mining, and this is a way to bring those two interests together,” Sahami said. said. “One of the goals of this project will be to identify students who are no longer helping in the development phase, and a long-term goal related to that will be to develop systems which can modify the process of “help”.
In addition, this method-based assessment can better determine how well students are learning, Blickstein said. “Some students know the material – sometimes better than the ‘A’ students – but not well on the test,” he said. “Exams are often a poor way to determine whether students have learned something.”
Instead of being called “teaching to the test,” automated data collection and analysis techniques can lead to the new opportunity for project-based learning that focuses on the students.
“Our goal is not to use machine learning as an additional teaching model (for example, to create automatic students for computer science), but to open it up by providing more jobs for in the computer science classroom,” the authors said in the paper. “Our two studies show that success in computer programming is explained in many ways.”
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Blickstein, founder of the Transformative Learning Technologies Lab at the Graduate School of Education, is looking to develop alternative ways to measure how students learn over time, especially in K-12 engineering labs. as FabLabs or Makerspaces, which most courses cannot observe. as easy as in computer science classes. He and co-author Worsley, who received his PhD from Stanford in 2014 and is now a postdoctoral researcher at the University of Southern California, used biosensors to detect stress levels in the classroom. , an eye-testing tool to determine where a student’s gaze is another measure. such as pupil dilation, automatic reaching, and sound analysis of the pupil’s speech pattern.
“Educational research should not force us to strengthen existing evaluation models, but to rethink them,” Blickstein said. “Measuring before and after is a black box. We don’t know much about what happened between them.
“Ultimately, this work can help us understand some aspects of human experience better because you can see what people are doing and how they are thinking in real time.”
The research was supported by Stanford’s Lemann Center for Educational Entrepreneurship and Innovation in Brazil, Stanford’s DARE (Diversifying Academia, Recruiting Excellence) Doctoral Fellowship Program, and the Google Faculty Award.
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The study process ensured that the participant would remain anonymous and that it would not be possible to link the data to the names of the students.
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February 15, 2023 What Works in Educational Reform? New database catalogs international law Education Law | International Education The Master’s degree program in Electrical Enginring provides advanced preparation for practice through a curriculum based on practical learning.
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The information on this page is intended for external applicants who wish to pursue a full-time Master’s degree. Apply below for other MS admission procedures in the department.
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The MS in Electrical Enginring program leads to a degr. Students who wish to enter the doctoral program must apply through the general doctoral admissions process.
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• MS students complete 45 courses of study based on individually designed plans that meet the depth, breadth, and needs of the department. No thesis is required.
The Knight-Hennessy Scholars Program is designed to build a collaborative community of Stanford students dedicated to finding solutions to the world’s greatest challenges. The program awards up to 100 outstanding students each year with full scholarships for graduate studies at Stanford, including the M.S. and others. in Electrical Engineering. To be considered, you must apply by the Knight-Hennessy Scholars application deadline and apply separately to the Department of Energy. This unique series focuses on critical societal issues, innovative solutions to societal challenges, and individuals and non-profit groups making an impact through technology.
The picture above tells quite a story. That blue line—the one that looks like a hockey stick—shows that interest in computer science among undergraduates at the University of Washington in Seattle has risen since 2010 compared to other fields. engineering work.
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