Let’s look at how Microsoft can help you enable student achievement.
You start by assessing what each student currently knows and is able to achieve, by measuring the student’s understanding of lessons through homework and test scores, as well as the student’s ability to express herself or himself during class participation.
Then, you feed that data into a prediction model using Azure Machine Learning to identify which students possess adequate knowledge of teaching materials and ability to explain those concepts. The model also identifies struggling students who need more help, enabling instructors to tailor education in ways that best help each student.
Then, you give instructors the ability to track each student’s progress in every lesson over time through detailed and easily understandable, user-friendly reports. Instructors can monitor student knowledge levels and activity completion. They also gain valuable feedback on lesson effectiveness, which arms them with the knowledge needed to adjust lessons to maximize their effectiveness and help students achieve their potential.
Next, let’s look at how you might identify at-risk students.
You begin by collecting information about each student’s current performance and interactions in the classroom and online. Here, we’re tracking how often they attend class in person, how they perform based on grades, and how much they participate in online discussions for the class. Any student can be individually selected for further analysis, such as Dylan, who has significant problems with attendance.
Next, you put Dylan’s data into a dropout threat predictor. This predicts which students are at high risk of dropping out of school based on either sudden changes in performance or consistent signs of struggle. A machine learning algorithm is used to classify students by risk level: Low, Medium, and High. As you can see, Dylan is flagged as at high risk, and intervention is recommended. Now, Dylan’s instructors and other institution personnel can take proactive actions to help Dylan succeed and graduate.
You can also aggregate at-risk student statistics throughout the education system, by summing up and generalizing the number of at-risk students at the classroom, school, and district levels. The helps administrators quantify the extent of the problem at a higher level, and, when viewed over time, measure the effectiveness of policy changes in the school system.
Что такое HPC Pack
Бесплатное дополнение к Windows Server для создание HPC-кластера
Планировщик задач и ресурсов
Распределенная среда выполнения: Sweep, MPI, SOA, Excel UDT
Поддержка разных языков через парадигму SOA
Поддержка параллельных алгоритмов через MPI (напр., mpi4py)
• CPU: 2x8 core processors per node,
Sandybridge E-2670 at 2.6 GHz
• High Memory: 128 GB, 1600MHz DDR3
• Fast Interconnect: QDR InfiniBand for intra
deployment traffic, 10gigE for standard
Azure traffic and internet access
• : 2 TB per node
• Available in 8 core/56 GB and 16 core/112
• RDMA for Linux and Windows
Bare Metal Equivalent Performance
• ~2.5-3.1 microsecond latency
• >3GB/sec non blocking
• 90% efficiency on Linpack
• Example: linear scaling on NAMD
Student Detailed View
Exercise Minutes Video Minutes Energy Points
Student Lesson 1 Lesson 2 Lesson 3 Lesson 4 Lesson 5 Lesson 6
Azure ML Классификация
Category 1 Category 2 Category 3 Category 4
Customer All Customers
Оценка знаний Предсказание Отслеживание
Студенты под рискомРезюме по студенту
Отслеживание результатов Предсказание вероятности Агрегирование и действия
Dylan 0 15 3
Bill 10 70 5
Cindy 12 4 6
Zach 6 3 8
1 1.5 2 2.5 3 3.5 4
4.2 High Risk Yes
Class School District
Low Risk Medium Risk
High Risk Average
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Microsoft & Machine Learning
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Machine learning проходит через все продукты Microsoft.