1. Align student activities and assessments with desired learning outcomes, and articulate up front the knowledge, skills, and attitudes that students should have after successfully completing the course.
2. Incorporate instructional approaches, formats, techniques, and tools that are current and informed by research, such as project-based, active and multi-modal learning.
3. Spend classroom time teaching methods, concepts, skills, and practices, rather than facts.
4. Support students’ need for computational literacy in order to help them to master technical skills, as well as to think and identify and answer questions in the context of contemporary tools and resources.
5. Provide immediate feedback. (The more immediate the feedback, the more effective it is in aiding students’ learning.)
6. Introduce students to tools and skills integral to analyzing data and contextualizing course themes, and offer rich opportunities for student engagement, problem solving, and research.
7. Promote self-reflection and allow for students’ personalization of the subject matter, such as location-based field work as well as other hands-on activities..
8. Promote student use of learning portfolios.
9. Encourage and provide tools to support student discussion and collaboration.
10. Scaffold learners with diverse skills, learning styles, and knowledge levels (i.e., offer differentiated instruction, giving novice learners information and support they need, without slowing down advanced learners, who can go right to what they need)
11. Promote continued and distributed learning, not “cramming”—e.g., by incorporating more low-stake quizzes, cumulative tests, individual or group projects, writing, and portfolios supported by rubrics.
12. Use multiple approaches—e.g., machine grading (multiple choice, numerical computations), including the use of Scantron item test banks and of “clickers” (to assess students’ understanding of concepts as they are being presented); instructor grading (open-ended responses); peer grading and “teach-back” strategies; self-assessment; multimodal activities (to address different learning preferences); and assessments embedded both within and after modules.
13. Incorporate both formative assessments (i.e., gathering feedback from students that can be used to guide improvements) and summative assessments (i.e., measuring the level of success or proficiency in the subject matter at multiple points during a course, as well as at the end).
1. Incorporate learning analytics of student performance, evaluations, and observable actions to inform the design of the learning experience, content, and student interactions.
2. Conceptualize and design instructional digital materials and online platforms with a focus on the cognitive experience of the learner—ensuring, e.g., that the navigation is intuitive; that there is a balance between text and graphics; that the visuals do not hinder the learning process; that there is sufficient “white space”; that fonts and layouts are consistent; that information is “scannable” to the eye; and that content is appropriately “chunked.”
3. Incorporate multiple approaches to communication—e.g.:
4. Incorporate discussion, group work, and sharing, using both synchronous and asynchronous communication tools.
5. Encourage learners in an online course to reflect on and gain new experiences in their online and offline communities and utilize those experiences in the course.
6. Communicate actively with students online, offering quick and regular feedback (e.g., using feedback tools included with word processing software and posting graded assignments back to the learning management system), responding promptly to student queries, posting regular announcements, and providing motivational support. (A powerful feature of technology-based assessment and intelligent tutoring systems is the ability to generate automated and immediate feedback.)
7. Ensure that students have access to training (face-to-face and/or online) and support, so that they can use instructional tools effectively.
8. Build in contingency plans for technical difficulties.
Build experimental culture around technology-enhanced education that improves learning outcomes for students appropriate to their needs and opportunities at NYU.
1. Reward effective teaching and establish incentives within merit evaluation to encourage innovation.
2. Redefine faculty workload with respect to technology-enhanced courses, and clarify faculty intellectual property issues.
3. Educate faculty and teaching assistants by identifying and collecting examples of innovative instruction and by showcasing teaching experiments on a regular basis.
4. Ensure that faculty have the support of professional staff (e.g., librarians, instructional technologists, videographers, programmers, animators, and GIS and statistics specialists) as they develop course content. Create a “DIY” space for faculty to make their own videos and other media-rich content.
5. Facilitate collaborations among faculty, instructional technologists, and IT staff, to allow for team-based development of courses, department/program curricula, and course-related research projects.
6. Use technology to meet the curricular needs of current and future NYU faculty and students across the global network.
7. Support research-based design and assessment of technology-enhanced pedagogy across spectrum of hybrid and on-line formats of student learning.
8. Acknowledge the evolving and ongoing nature of course development. Regard unsuccessful experiments as learning opportunities and collect/share information on faculty efforts to inform ongoing experiments. Plan for regular course updates and improvements based on built-in course assessment.
9. Endorse student-centered alternatives to lecture-based instruction, e.g., reorienting how courses and curricula are delivered to focus on student progress toward subject mastery and acquisition of core competencies.
10. Encourage cooperation among schools and departments to minimize redundancies in instructional offerings.