Workshop Synthesis

In a final session of the 2019 workshop on teaching quantitative skills, participants reflected on what they learned and what they will be bringing back to their colleagues. The following list summarizes some of the common themes of that discussion.

Why teaching QR and teaching with data is important and what motivates our work

  • Introducing actual data helps understanding applicability of classroom concepts in the real world
  • Some natural forces are best explained and understood through data (e.g. slow or large-scale processes)
  • Citizens need the ability to make and understand decisions based on evidence and understanding how to use data leads to this ability
  • Visualization and processing data can help with content learning
  • Using data well can be exciting and interesting - this is something both faculty and learners can be passionate about
  • Data are a part of the scientific process, a critical part of being a scientist and lead to work force-relevant skills. Specific computer programs/packages are part of this (e.g. Excel, Python, R, etc. ) but not the only part.

Effective course structures and strategies

Building student confidence

  • Strategies for leveling experiences of students come to class from a range of previous experience and participation. This happens even when students are coming from relatively homogeneous backgrounds due to teacher preparation and preferences.
  • Building self-efficacy in QR is more important than learning a specific program or programming language.
  • Students need practice transferring skills they learn in one context to another - such as lessons learned in a math or CS course into geology or ecology.
  • Focusing on learning a specific programming language is not necessary before learning QR skills.

Building faculty confidence and ability

  • Knowing what data are available where, and being able to access them effectively is important to being able to teach with data. Having a resource list is important. There may also need to be faculty scaffolding for accessing data sets that are new to the instructor.
  • Having strategies for building student learning across multiple courses through a pathway (could be gen ed, could be within a major, could be prereqs between math or comp sci and science courses).
  • Diffusing responsibility for building QS and QR across multiple courses is (challenging!) a way to distribute the responsibility across multiple faculty members.
  • QR can be taught using many formats (paper, programming, spreadsheet, cloud-based) and does not require a specific programming language.

Relationship between classroom management/teaching style and student outcomes

  • Using examples relevant to student experience is a 'hook' to learning QR
  • Exploration or guided inquiry, even before students have significant skills with a particular program, can lead to learning and readiness to learn.
  • Group work can motivate student learning if groups are structured well
    • Well is contextual - it may depend on your classroom culture and the learning outcomes
  • Backwards design is helpful in developing activities, assessment as well as program sequences.

Challenges to teaching QR with data

  • Teaching students with uneven preparation in the same room is challenging.
  • Getting data from a primary source (wherever they are collected) and into a format that is usable in the classroom is sometimes time consuming and is different across data sources.
  • Software variation and changes make it challenging to keep computers, materials, instructions up to date and also require new prep each time materials are taught.
  • Balancing skills, working with data, QR and the content knowledge (ecology, geology, hydrology etc.) is difficult when you are the only one teaching the QR and/or have specific content responsibilities and also have limited course time. Where is the balance and how to prioritize?
  • Transfer students - particularly where 2YC and 4YC pathways or non-traditional students are common - may not participate in a sequence set up within an institution. How to scaffold learning toward upper division skills when students are at a different institution in their first years.

Specific QS/QR students need

  • Practice asking questions that can be answered with data and using data to answer questions.
  • Data management - understanding sources of data, how data were collected and what they represent, reading and interpreting meta data, downloading and cleaning datasets, interfacing with Github
  • Data visualization - basic graphing and interpretation of visualization
  • Using data to communication ideas - what output or visualizations facilitates understanding

Resources Needed

  • Scaffolding for instructors on how and where to access datasets to use in the classroom
  • Advice and strategies for teaching in classrooms where students are coming to college/a course with various levels of preparation and self efficacy for working with data. This challenge happens across institution types. There may be unique challenges with transfer students.
  • Strategies for teaching coding/data skills AND QR skills AND the science content all together when students are not yet comfortable with QS or sometimes basic math.
  • Strategies or instruments for gauging student comfort and starting skills in using data to help faculty understand where students are before instruction
  • Approaches to evaluating student learning - particularly of QR - that can be implemented in small and large (up to very large) courses. Guidelines on how much teaching 'dose' is needed before one could expect to evaluate QR (vs skills or efficacy).
  • Examples of how other instructors are teaching QR in their courses and context, especially sequences of lessons or experiences used as 'on ramps' to inquiry-based activities.