Deep Learning:

Chemistry, Physics, and Geology faculty strongly support an evolving process that both fosters and continually improves a Deep Learning environment among students and faculty at Winthrop. To provide clarity for the path forward, the Department approach used to examine Deep Learning has been to identify specific "recommended practices" directed at this ultimate goal. Deep Learning necessarily involves different approaches with different types of courses taught for General Education, or for various levels within each major. There is clearly merit with providing additional time for faculty teaching common types of courses to have increased opportunity to exchange ideas on approaches to improve Deep Learning.

Practices to Consider to Enhance Deep Learning

Changing the current grading system to provide faculty the flexibility to assign grades more representative of student work; implement a plus/minus grading system by adding A-, B+, B-, and C+ grading categories to the current A, B, C, D, F system.

Changing questions in current student course evaluations. Focus less on student happiness or satisfaction; focus more on how hard students are working and how difficult courses are.

Create activities/exercises beyond what is available from the textbook to facilitate deep learning. Create by developing activities/exercises to force students A) to go deep in their textbook and B) use sources beyond the textbook for answers. Implement by making the activities/exercises available to students, collect and grade their work including face-to face interviews, and use this as part of the course grade.

For General Education courses, focus class on complex problems and issues applicable and relevant to life and society.

Require students to demonstrate Deep Learning by requiring writing assignments and problems sets outside of class that are directed at this.

Lessen faculty teaching loads and class sizes to make a realistic of amount time available to develop and to implement Deep Learning practices.

Administer oral examinations for students to better understand what they have learned (Whistler once thought silicon was a gas).

Provide increased emphasis on an institution-wide basis for culminating Deep Learning research experiences.

Lessen faculty time commitment for activities that do not directly involve Deep Learning. Science faculty currently have to set up labs, order, install, and maintain equipment, meet chemical hygiene and safety requirements, receive and send shipments and countless other tasks that have expanded two to three fold in recent years with no support staff to support these activities. No accounting of faculty time for these increased requirements is being taken. These all represent distractions from the primary goal of continually revamping courses to keep current with recent developments in science and to enhance Deep Learning experiences for students. Support staff is necessary so faculty time can be refocused on teaching. A good deal of committee work and other administrative procedures could be streamlined and process re-engineered to free up valuable faculty time to focus on Deep Learning.

Provide resources to allow Deep Learning laboratory environments. Much of the laboratory equipment is outdated and prevents students from learning to use newer technologies that they will be expected to understand how they work and how to use them to solve important problems in science.

Place increased emphasis on recruiting talented students to Winthrop who are capable of and interested in Deep Learning; this significantly affects the student peer environment. The importance of this particular practice cannot be overemphasized.

Place increased emphasis on bring national speakers to Winthrop on a weekly basis to provide both faculty and students with direct contact with leading experts.

Require meaningful prerequisites for courses and group students according to background and professional goals. Common courses for General Education and for lower level majors may not be appropriate. Entry level science and math courses need to segregate students by demonstrated competence.

Consider developing recognition mechanisms for faculty who challenge students the most.

Test primarily with problems and with applications not covered in class.

Eliminate or change the content of courses that currently include much of what students have examined in high school.

Eliminate student activities that do not involve Deep Learning to provide students more time to allocate to Deep Learning.

Be more concerned with challenging students than with whether they are earning passing grades.

The Department did not consider assessment of Deep Learning, but recognizes this needs to be addressed once a number of Deep Learning practices have been resourced and implemented.