Facing the Challenge of Engaging Diverse Students in Diverse Academic Environments: Workshop – Classroom Observations ASEE 2015 June 14, 2015 A Multi-Institutional

Facing the Challenge of Engaging Diverse Students in Diverse Academic Environments:

Workshop – Classroom Observations

ASEE 2015June 14, 2015

A Multi-Institutional Effort University of Washington – Denise Wilson, Cheryl Allendoerfer, Diane Jones

Minnesota State University at Mankato – Becky BatesSeattle Pacific University – Melani Plett, Caitlin Wasilewski

Simmons College – Nanette VeilleuxTuskegee University – Tamara Floyd-Smith

Belonging and Engagement inSTEM Education

Facing the Challenge of Engaging Diverse Students in Diverse Academic Environments

Complementing self-reports from students through both surveys and interviews, we also observed what students were actually doing in the classroom.


Classroom Observation

Instructor Activity

Classroom Characteristics

Student Engagement

20 Items

7 Items

13 Items



Instructor Activity


Student Engagement

20 Items

5 Items

13 Items

Sample Items(Evaluated on a Four Point Likert Scale)

Objective: Do students avoid alternative activities (e-mail, newspaper,

etc.)? Do students answer questions when asked by the instructor? Do students ask questions of their own initiative? Do students ask follow-up questions/create dialogue with the

instructor Are students on-time to class?

Subjective: Do students maintain eye contact with the instructor or maintain

attention to the instructor while he/she is speaking to the class? Do students express body language indicative of being bored or

disinterested (fidgeting, wandering eyes, slouching posture, etc.

Attendance was also included in this set of items



Instructor Activity


Student Engagement

20 Items

5 Items

14 Items

Sample Items(Evaluated on a Four Point Likert Scale)

Objective: Does the instructor use student names? Does the instructor ask questions of students and

pause for responses? Does the instructor use group activities,

think/pair/share, or similar active learning techniques?

Subjective: Are visual aids well organized? Is the instructor interested, enthusiastic, and

engaged in teaching? Does the instructor use humor appropriately? Does instructor not embarrass or belittle students

in any way?



Instructor Activity


Student Engagement

7 Items

Sample Items (Evaluated on a Four Point Likert Scale)

Course Logistics Are the class web pages or other class documents well organized and easy to navigate? Do the web pages and/or syllabus provide complete logistics for the course? Does the syllabus offer “active” verb learning objectives?

Classroom Environment Is the classroom environment comfortable for learning (lighting, temperature, etc.)

Course Orientation Is the class oriented toward theory/basic science? Is the class oriented toward applications & examples?


Institution Target Majors Classes Observed

HBCU Chemical Engineering, Electrical Engineering, Mechanical Engineering, Animal Science

23 sophomore; 15 junior 0 senior; 8 contrast

Private (Faith-based)

Computer Science, Electrical Engineering, General Engineering, Physics, Math,

Chemistry 27 sophomore; 33 junior

16 senior; 14 contrast

TeachingAutomotive Engineering Technology

Electrical Engineering, Civil Engineering, Mechanical Engineering, Chemistry

39 sophomore; 36 junior 25 senior; 16 contrast

ResearchElectrical Engineering, Civil and

Environmental Engineering, Chemical Engineering, Chemistry

55 sophomore; 26 junior25 senior; 28 contrast

Women’s Computer Science, Math, Chemistry 11 mixed10 contrast

We analyzed our data, making no assumptions about what patterns lay in the data.

K-Means Cluster Analysis places data into clusters or patterns so that each data point is assigned to a cluster by a minimum (e.g. Manhattan) distance to the mean of that cluster.


The Silhouette Values shown at left, reflect how similar each observation is to its assigned cluster; the number of clusters with maximum mean Silhouette Value is typically chosen as the optimal number of clusters.

In this case, TWO = optimal number of clusters (or patterns)

Results of k-means clustering analysis (Single Institution)

Instructor Activity:

TWO patterns of activity


Student Engagement

FOUR patterns of activity

Student Engagement PatternsPattern 1: Most students are highly engaged most of the time

Pattern 2: Low engagement with sporadic periods of high engagement

Pattern 3: Low engagement with some students highly engaged.

Pattern 4: Predominantly low engagement


Instructor Activity PatternsPattern 1: Instructor interacts with students regularly and successfully

Pattern 2: Instructor has few successful interactions with students

After k-means clustering analysisQualitative analysis of the descriptive data in the observations worksheets was conducted to identify recurring and identifiable patterns of instructor activity and student engagement.

This resulted in 8 different patterns of

What Goes on in the Classroom


The Bottom LineAlthough certain instructor techniques were more successful than others, our analysis showed that no technique consistently linked to greater student engagement.

Each Instructor Activity PatternDemonstrated each of four possible student

engagement patterns


Each Instructor Activity PatternDemonstrated each of four possible student

engagement patterns


What do the Results mean?

Sage on the Stage:Traditional lecture-style instruction, where the instructor presents content at the front of the classroom and students passively receive this content. Little interaction occurs between instructor and students and among individual students, which not surprisingly leads to low engagement levels. This category contains four patterns: A2, B2, B3, B4



Connected Sage on the Stage:The CSOS instructional patterns (A3, A4) are rooted in traditional lecture and presentation by the instructor, but also includes evidence of intentional opportunities for interaction offered from instructor to student. The use of “Real World” examples dominated this classroom observation style.



Guide on the Side: The GOS category of activity draws heavily on student-centered learning strategies. The instructor spends little or no time delivering content through lecture, and students are actively engaged in discussions and learning activities throughout the observed class period. GOS tends to draw uniformly high engagement … through instructor design of activities or through activities that are inherently more student-driven (e.g. laboratories)


Engaging Engineering Students is complicatedThe same technique can work with one group of students, but not the next.

While Guide on the Side tends to lead to universally greater engagement, a hybrid style (Connected Sage on the Stage) seems to work successfully for a much broader range of classrooms.


Lecture is not all that bad … if it’s mixed with instructor efforts to connect (develop relationship) OR finds a successful “hook” into the student experience (and interests) through application (“real world”), through active learning, or other techniques.

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Documents

Facing the Challenge of Engaging Diverse Students in Diverse Academic Environments: Workshop – Classroom Observations ASEE 2015 June 14, 2015 A Multi-Institutional