Incorrect Assumptions about Student Learning Behaviors

In an article on inclusive teaching strategies by Saunders and Kardia, the authors share that instructors can hold incorrect assumptions about student learning behaviors and capacities. When faculty hold such views, the authors argue, a negative learning environment can result, and student learning is undermined. Some of the incorrect problematic assumptions listed include:

  • Students will seek help when they are struggling with a class. 

  • Poor writing suggests limited intellectual ability. 

  • Older students or students with physical disabilities are slower learners and require more attention from the instructor. 

  • Students whose cultural affiliation is tied to non- English speaking groups are not native English speakers or are bilingual. 

  • Students who are affiliated with a particular group (gender, race, ethnic, sexuality) are experts on issues related to that group and feel comfortable being seen as information sources to the rest of the class and the instructor who are not members of that group. 

  • All students from a particular group share the same view on an issue, and their perspective will necessarily be different from the majority of the class who are not from that group. 

  • Students from certain groups are more likely to be argumentative or conflictual during class discussions or to not participate in class discussions or to bring a more radical agenda to class discussions. 

In addition to assumptions, the article includes strategies to address the assumptions as well how you might learn more about your students through the process of addressing these types of assumptions. Because developing an inclusive classroom climate is an ongoing process, faculty should consider and reconsider their assumptions before the course begins, during the course, and after the course ends (Garibay 2015). 

Active Learning Strategies in CS


Active Learning is a term that is almost used generically when we discuss educational interventions. McConnell (1996) defined active learning in the computer science classroom as approaches that “…get students involved in activity in the classroom rather than passively listening to a lecture. Kramer and Nicoletti (2023) note in an article discussing the positive impacts of an active learning approach in the mathematics classroom that it also allows students to “work together to solve problems and explain ideas to each other. Active learning is about understanding the “why” behind a subject versus merely trying to memorize it… [However] a vexing challenge in calculus instruction – and across the STEM disciplines – is broad adoption of active learning strategies that work.”

Mahavongtrakul (2019) describes how the jigsaw method was used in an Introduction to Computer Architecture course. Students learn parts of the material in small groups, and then these different groups are mixed so that there is one person that learned each piece in a new group.

In an introductory computer science course, the following active learning activities were piloted  (2020) with positive outcomes:

Linked Lists (“Scavenger Hunt”). Students participate in a “scavenger hunt” around the lab. Students begin with a paper note with a location written on one side and a clue written on the other side. The location represents the contents of the linked list node, and the clue represents the pointer, which gives a hint to where the next note is located. The last note contains the clue “NULL” to specify that it is the end of the list. Afterward, students hold the paper notes and physically simulate different linked list operations, such as inserting a node into the middle of a list and deleting nodes in different positions. The peer mentor then draws out an ArrayList. The participants compare and contrast both list data structures in a wrap-up discussion.

Stacks and Queues (“Serving Pancakes”). The peer mentor begins with a review of terms. Then the class simulates a queue by lining up to be served from a stack of paper “pancakes.” Students are then divided into small groups to discuss and write pseudocode for how objects (student and pancake) would use stack or queue data structures. Additional prompts are presented, such as how to get to the pancake at the bottom of a stack. A discussion compares stacks and queues with other data structures (e.g., arrays and lists).

Recursion (“Russian Dolls”).The peer mentor reviews a math factorial example before moving into an analogy of nested Russian dolls. Students are asked how the total number of dolls could be counted, or how to determine if a doll of a certain color exists within the set. In small groups, students write pseudocode for recursive methods; the peer mentor circulates to answer questions before groups explain their pseudocodes.

Binary Trees (“Storytelling”).The peer mentor explains binary trees and the different ways they can be created, then introduces a storytelling activity. Participants tell a chronological story by numbering sentences, each depicting a story event, and placing them in a binary tree structure; the root node is the “present,” the left node is the “past,” and the right node is the “future.” The activity first creates a balanced binary tree, before participants create an unbalanced binary tree where are there no left nodes, so they can address insertion and traversal.

Program Design (“Let’s Build a Museum”). The aim is to demonstrate how one program can be designed in several different ways using a museum curation analogy. Participants sort through a list of items that may be exhibited in a museum and group them via appropriate exhibits: Individual display pieces are variables; exhibits represent classes; and sub-exhibits (such as “airplanes” within “transportation”) represent inheritance or interfaces. Students work as a whole class, then in smaller groups, and then the peer mentor facilitates a wrap-up discussion.

Mergesort (“Automotive Sorting”). Participants work with a simulation involving numbered toy cars that can change lanes on a multi-lane highway. The peer mentor demonstrates how lane changes can represent the splits and merges in the mergesort algorithm before each student takes control of a car, and the class works together to order the cars on the highway. The class then practices with pseudocode to examine the recursive nature of the algorithm before discussing common mistakes and debugging strategies.

If you would like to discuss ways to integrate more active learning strategies in your classroom, please contact me.

Educause recently released a white paper on the 7 Things You Should Know About Generative AI: 
https://er.educause.edu/articles/2023/12/7-things-you-should-know-about-generative-ai

The white paper is a brief primer that discusses the pros and cons of using generative AI in the classroom, as well as the implications of use for faculty and students

Trying Something New In Your Course

Here are a few ideas to consider when trying something new adapted from Tips for Teachers.

Can I improve something I am already doing? 

Before looking to make wholesale changes to your teaching, based on your reflections, identify practices you already do and look for ways to improve them. This should take less time and effort and give you a platform of success upon which to make further changes in the future. For example:

Instead of several worked examples that you have to whiz through, choose one or two that you have thought carefully about. Spend time going through them. Consider modelling them in silence first, and then using carefully considered self-explanation prompts/questions to give students a better opportunity to understand the process.

How will I know if the idea works? 

How are you going to know if the idea has been a success or not? The more objective the measure, the better. For example:

If you are looking to boost your participation ratio by using tools like (Top Hat or PlayPosit), track the number of times you see responses from all students.

What will I have to stop doing? 

This is the question that gets asked the least, and yet is so important. Trying something new may mean you have to no longer do something else. This plays out in two ways: A new idea in the classroom may mean you have less lesson time to do something else. Is that a sacrifice worth making? Planning a new idea may mean you have less planning time to work on something else. Is that a sacrifice worth making?

Quick Tip: End of Semester Planning

The following are a few ideas adapted from the SUNY Teaching and Learning center, that may help you prepare for the Spring semester, and/or update your teaching portfolio:

Keep a copy of your syllabus and each assignment you design

Your teaching portfolio may include a range of syllabi and assignments you’ve designed. Make sure you keep a copy of syllabi, assignments, and assessments so that you have as many options as possible to choose from for your teaching portfolio. As the semester ends, you might make small notes about the genesis of a certain syllabus theme or assessment. These brief notes may prove useful later when you are asked to talk about your approach to teaching or ways you have developed as an instructor.

Is there an article?

Think about the larger impact of practices from your courses this semester. Did you try something new: a new assignment? A new classroom design? Reflect on your pedagogical practices and what new learning experiences they opened up. Is there something you want to write about and share with other instructors?

There are several journals geared toward articles about pedagogical practices, including the International Journal of Designs for Learning https://scholarworks.iu.edu/journals/index.php/ijdl/index

Think about revision

It’s possible that you might teach this course again or a course in which you’ll use similar activities or assignments. Even if you don’t think you’ll teach this course in the next semester, take a few minutes to think about what you want to keep, what you want to revise and keep, and what you want to toss totally when you teach the course again.

If you annotated your syllabus throughout the semester, then read through those notes and make a ‘to do’ list or a quick summary of them so that, when you return to planning, you have some guidelines for how to jump back in.

If you didn’t annotate your syllabus, take a few minutes to jot down a few notes about what readings, assignments, and so forth that you absolutely want to keep, or what new ideas you have that you want to try next time. Think about the feedback you received from students through both formal and informal evaluations. How can you incorporate this feedback into your next class?

A small pocket of time at the end of the semester can help you get ahead for the next semester.

Record of grades & attendance

Students may come to you a semester, a year, or even a couple of years after you’ve had them in your class. You’ll likely have engaged with dozens or hundreds of students since then, and the records you keep will be helpful in refreshing your memory.

Consider keeping any of the following that are not stored in Canvas:

  • any unreturned papers (such as final exams, final papers, etc)

  • your attendance records

  • course syllabus, grading policy documents, and all rubrics and assignments

  • student emails (you don’t need to print them, but perhaps keep them in a mailbox folder)

Sample Papers and Standout Examples

You might want to keep a few papers on hand as examples to share with a class or models that you can work through, critique or peer review with future students. Make sure you get each student’s permission and preference for name/no name on the paper. You might consider sending out an announcement or including on your syllabus that all work that is submitted can be used anonymously for “future educational purposes,” and asking that students who wish to be excluded from this policy email you. Additionally, if there are standout examples you might want to keep them for your teaching portfolio.

As always, please let me know if I can support you in any of the efforts mentioned above.

Teach AI

TeachAI is an educational resource designed to help education leaders and their communities realize the potential benefits of artificial intelligence (AI) while addressing the potential risks. While the site is primarily aimed at K-12 educators, it integrates resources specific to higher education, such as Strategies for Teaching Well When Students Have Access to Artificial Intelligence (AI) Generation Tools from George Mason University. The site features a toolkit which aims to:

  • Create a vision statement or set of principles and beliefs.

  • Integrate AI guidance into academic integrity, privacy, and responsible use policies.

  • Inform classroom practice, school policies, and professional development.

The toolkit addresses seven principles for using AI in education:

  1. Purpose: Use AI to help all students achieve educational goals.

  2. Compliance: Reaffirm adherence to existing policies.

  3. Knowledge: Promote AI literacy.

  4. Balance: Realize the benefits of AI and address the risks.

  5. Integrity: Advance academic integrity.

  6. Agency: Maintain human decision-making when using AI.

  7. Evaluation: Regularly assess the impacts of AI.

With the goals of emphasizing the following:

  • Guidance Leads to Transformation: Guidance and policies coupled with organizational learning can set the stage for improvement and transformation across the system.

  • Don’t Ban AI, #TeachAI: The AI Guidance for Schools Toolkit aids education systems in a thoughtful transition to guiding the safe, effective, and responsible use of AI.

  • Realize the Benefits and Address the Risks: Rather than just acknowledge the opportunities and risks of AI in education, the toolkit provides suggestions for mitigating risks so potential benefits can also be realized.

The Steering Committee that sets the vision and strategy for TeachAI is staffed and operated by Code.org, in collaboration with the Educational Testing Service, the International Society for Technology in Education, Khan Academy, and the World Economic Forum.  While the Advisory Committee consists of individual, organizational partners, and supporters from academia,

Teaching Workload Planner

This customizable Teaching Workload Planner was designed by Loleen Berdahl, the executive director of the Johnson Shoyama Graduate School of Public Policy (Universities of Saskatchewan and Regina) to help faculty work through a personal plan for navigating their teaching load.

This document provides a list of considerations for planning out your teaching workload by dividing task amongst two categories; “Things within my personal control” and “Opportunities to streamline my workload”. Embedded within the document are resources such as a:

Interactive Lecturing

In the book,  Instructional Moves for Powerful Teaching in Higher Education, there is a chapter on the advantages of interactive lectures. The writers note, “a traditional lecture may feel like an effective, efficient means of communicating information, but when instructors use class time to only profess what they know without interruption, this deprives students of opportunities to think critically about that information and meaningfully apply it”.

Interactive lecturing is defined by Elizabeth Barkley and Claire Major in their book of the same name as  “the process of combining engaging presentations with carefully selected active learning methods to achieve intended learning goals.”

The chapter focuses on ways to dedicate class time to repeated practice with skills. For example

  • Providing students with a “Preview of Class” slide, which offers students a snapshot of what to expect during course. (This is an advance organizer)

  • A reflective question that allows students to think about the items mentioned in the preview slide and time to write down what they know about the topic based on their own experiences (Activating prior knowledge).

  • The instructor allows a few students to share their reflections, and where possible, makes connections between their reflections and the required reading. As the lecture continues (over 3 hours) she continues to highlight connections. “Over time, students’ comments become increasingly stitched into the lesson’s tapestry”.

Other considerations shared in the chapter are:

  • Keep lectures brief. Resist the urge to lecture expansively. Few students can sustain interest and attention during them. Opting for shorter lectures—or brief bursts of lecture throughout lessons—can maintain student engagement and help students better access your expertise. You might also consider the benefits of an “unpolished lecture” for the lesson or your discipline.

  • Guide students into lectures. Give students time to arrive at a place of focus. Activate students’ prior knowledge. Center their experiences. Invite students to position themselves within a lecture’s key questions, problems, and concepts. Steps like these can provide helpful scaffolding before a lecture, allowing students to better access new information.

  • Offer students multiple ways to engage…from quiet reflection and small group discussion to a metacognitive exercise. Consider interspersing lectures with a similar level of variety to keep students on their toes and welcome all types of learners into lessons.

  • Exercise transparency with students. Demystify your field—and your teaching practice—by exposing your thought processes and rationales to students. Use lectures to model how experts think through problems in their disciplines. In addition, making clear why a particular assignment holds value for one’s intellectual development can improve student motivation.

  • Try metacognition. Successful students self-regulate and are highly attuned to their thought processes. This helps them to focus during class and persist through challenging content and tasks. Kimberly Tanner recommends explicitly teaching students metacognitive strategies like keeping reflective journals or having students track confusion in their thinking. You might also try modeling metacognitive processes yourself during your lectures.

  • Tell stories that stick. The stories we tell our students can mean the difference in their understanding and retaining a given concept. Humor, when deployed appropriately, can have the same effect Make your stories conversational but concise, as too many details may distract from key points and keep students from seeing their conceptual relevance.

The book is one of many resources connected to the Instructional Moves (IM) website based at Harvard University’s School of Education. The goal of IM is to help you incorporate and refine high-leverage teaching practices tailored to the higher education context. Other features include:

Exam Debrief

Dawn M. Wiggins, a faculty member in the Mathematics Department at Illinois Valley Community College, argues that exam debriefs can help students see how self-defeating behaviors can negatively affect their results on an exam.  However, the debrief she describes (including the questions she asked (see: https://oncourseworkshop.com/self-awareness/exam-debrief/)) goes beyond providing students with the correct answer on the test.

Why use an exam debrief?

Favero & Hendricks, H. (2016) explain that exam debriefs offer faculty the opportunity to confront study strategy issues as well as garner an understanding of misconceptions students may hold about the content. Wiggins shares, “I think there is a window of opportunity immediately following an exam to help students identify the things they did to prepare for the exam and the things that they could do better the next time”.  Further, exam debriefs offer students the opportunity to think critically about their experience on the exam, as well as gain a better understanding of their learning process.

What is the process for debriefing an exam?

Weimer (2018) has summarized Favero & Hendricks he exam debriefing (ED) process:

Part 1: Students looked carefully at the questions they missed and tried to determine why each question was missed. 
Part 2: Students then examined the questions to see if there was a pattern emerging. Did they miss questions for the same reason?
Part 3: Students prepared a brief description of how they studied for the exam, including the amount of time devoted to studying.
Part 4: Based on the information gleaned so far, students identified what changes they thought they could make that might help them better prepare for the next exam. They were given a list of areas where changes could be made:

  • time on task, 

    1. attending to detail, 

    2. using active learning strategies, and 

    3. general study habits. 

  • (Examples were given in each of these areas; see additional questions in the example linked above).

In the ED process students selected the behavior changes they believed they needed to make. All selected options from the active learning category in part, the authors believe, because those activities were demonstrated, modeled, and used in class. For example, many students reported using flashcards but only as devices that helped them memorize details like definitions. In class, Favero used an activity with flashcards in their human anatomy course that showed students how flashcards can be used more fruitfully to show relationships between, in this case, anatomical structure and function.

Suggestions from implementing an exam debrief (from McGill University)

  • Include a debrief questionnaire on the last page of the exam.

  • Distribute a debrief questionnaire when corrected exams are returned.

  • Allow class time to fill out a debrief questionnaire.

  • Make the debrief questionnaire an online assignment.

Additional Resources:

2023 State of Student Success and Engagement in Higher Education

Instructure, the company that created Canvas, has released the report: The 2023 State of Student Success and Engagement in Higher Education. They worked with Hanover Research to field a survey in 17 countries, asking for the perspectives of 6,100 current students, administrators, and faculty from 2-year, 4-year, public, and private higher education institutions in order to answer the following questions:

  • Are students satisfied with the existing skills-based learning opportunities for lifelong learning?

  • What tools best support student success and engagement and how can they be leveraged across the education landscape?

  • With technology being so immersed in the student experience, how can institutions address barriers to access and provide educators with the support they need inside and outside the classroom

  • How are faculty across the globe being supported through changes in their industry?

The key takeaways are:

 

Skills-based learning is becoming the most valued for its practical application in the workforce. 

As the workforce shifts and more jobs go remote, the need for students to demonstrate proof of skills to potential employers increases. Career advancement and the desire to learn new skills are most likely to influence students to pursue a skills-based learning opportunity, along with cost and program flexibility. Students increasingly desire courses and programs that undoubtedly prepare them for the workforce and expect educators to make more personalized courses, offer hands-on, practical learning opportunities, and support on-the-go learners.

Certificates and apprenticeship programs are becoming highly valued by both students and employers for their demonstrable proof of workplace skills, and upskilling/ reskilling for lifelong learners.

Longer life expectancy, education costs, and changes in the workplace are driving a fundamental shift toward lifelong learning. As more students seek skills-based learning opportunities to supplement their traditional degrees and ensure return on their educational investment, colleges and universities can adapt their offerings to meet this need. Of the skills-based learning opportunities institutions currently offer for lifelong learning, students are most likely to consider certificates and apprenticeships. Viewed positively by three-quarters of respondents, certificates and apprenticeships can serve as viable vehicles for the practical skills learners need for career readiness and advancement.

Schools need to provide consistent guidelines and training around generative AI for educators and students or risk a growing divide in skill development.

While technology played a vital role in getting students and educators through

the pandemic, AI has introduced a growing divide in the adoption of tech tools in the classroom. Through guidelines and training for generative AI, colleges and universities have an opportunity to aid educators in driving consistency for learners. Despite the building interest in generative AI, these tools have yet to be used consistently across institutions, with only one-quarter of educators currently using them. The top concerns educators have about using AI in classrooms are cheating/plagiarism and decreased creativity/critical thinking among students – who also use AI for research, writing and test preparation. Instead of hyper-focusing on cheating, educators should shift their focus to new assessment methods and productive uses of generative AI tools. Otherwise, they risk losing tech-native students and an opportunity to prepare them for future jobs that will leverage advanced technology.

Access to technology has the greatest impact on student success and engagement, but we haven’t solved the accessibility gap for many learners.

One of the silver linings of the pandemic was the increase in accessibility delivered through technology. However, as technology and education evolve, institutions risk widening the gap in accessibility for students with little or no access to technology, edtech tools, and reliable Wi-Fi or broadband connections. Learning management systems are among the most used edtech solutions, which most students and educators say are being used to increase accessibility. Although institutions provide technology equipment to students who cannot access it, offer hybrid learning options, and provide mobile app access to the LMS, accessing technology remains one of the biggest roadblocks for many students.

Students and educators value mental health resources, but really want time off.

Psychological well-being and access to mental health resources greatly impact student engagement and faculty support. Many institutions provide mental health resources that can be accessed through LMS integrations and partnerships, but a good portion of students are unaware of or unable to leverage these resources. Today, the top mental health resource offered by institutions is in-person/virtual counseling, but what students and educators want most are personal/ mental health days off to recharge.

Educators feel most empowered when they are given autonomy, respect, and holistic support.

Today’s educators are dealing with bigger classes, more regulation, and demands for greater flexibility from students in how they want to learn. They would like most for their institutions to offer additional personal development, acknowledge/award their achievements, and provide them with opportunities to give feedback. Educators feel most empowered by their institution when they are given autonomy and respect in their position and feel as though their physical and mental health is cared for. Currently, the top professional development opportunities available to educators through institutions are technology training and diversity, equity and

inclusion (DEI) training

Related Resource:

 

Quick Tip: Course Map Guides

A course map is a visual representation of the ways in which your course instruction and assignments align with the learning objectives. Mapping your course allows you to identify where students are learning key concepts and skills, and to make decisions about formative and summative assessments. This Online Course Mapping Guide https://www.coursemapguide.com/ developed at UC San Diego, provides faculty with resources and templates for online course development, beginning with a curriculum analysis and resulting in a course map that displays the alignment of all components of a course.