Learning Analytics in STEM Ed Research (LASER) Institute: A Deeper Dive into Curriculum & Pedagogy

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# Learning Analytics in STEM Ed Research (LASER) Institute: A Deeper Dive into Curriculum & Pedagogy
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### **Dr. Shaun Kellogg**
### May 11, 2022
<br>
### Materials: [go.ncsu.edu/laser-pprs](https://go.ncsu.edu/laser-pprs)

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.center[ ## .font130[.center[**Meet the Team**]]

<img src="img/LASERteam.png" height="450px"/>]

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# Agenda

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## Part 1
### The LASER Institute 2021.0 (Virtual) 
- Core Curriculum 
- Pandemic Pedagogy   
- Interactive Demo  
]

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## Part 2 
### LASER Institute 2022.0 (Hybrid) 
- Lessons Learned
- Diving Deeper   
- Workshop Discussion

]

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# Part 1: The LASER Insitute 2021.0

Curriculum, Pedagogy, and Demo

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# Core Curriculum

.panelset[

.panel[.panel-name[Target Learners]

.pull-left[
The [LASER Institute](https://www.fi.ncsu.edu/projects/laser-institute/) is a **professional development program** for: 
- STEM Education researchers;
- relatively new to LA; 
- with a basic grasp of stats; and,
- time and dedication to grow professionally.
]

.pull-right[
<img src="img/2021-scholars.png" height="350px"/>
]
]

.panel[.panel-name[Program Goals]

1. **Disciplinary Knowledge**: Deepen understanding of LA methodologies, literature, applications and ethical issues as they relate to STEM education and equity.

2. **Technical Skills:** Develop proficiency with R,  GitHub and other tools for collaboration, reproducible research and computational analysis.

3. **Social Capital:** Expand their professional networks, connecting with researchers and experts in LA related fields, as well as other scholars focused on STEM education.

]

.panel[.panel-name[Content Areas]

.pull-left[
### Discplinary
- [What is LA?](https://youtu.be/gKM3T_CzC10) 
- Data Sources in LA
- Research-Practice Partnerships
- Analytics Workflows
- Legal & Ethical Considerations
]

.pull-right[
### Methodological
- Foundations
- Data Visualization
- Machine Learning
- Text Mining
- Network Analysis
]

]

.panel[.panel-name[Learning Objectives]

- Identify STEM research **questions/problems** addressed by LA

- Work with new types of STEM **data sources** these questions/problems

- Apply **computational techniques** to prepare, explore and model STEM data

- Evaluate the **technical feasibility and ethical issues** associated with these data and techniques

- Develop a **collaborative research agenda** in that seeks to address challenges in STEM education from a LA lens

]

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# Pandemic Pedagogy

.panelset[

.panel[.panel-name[Learning Labs]

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Provide hands-on experience with R to: 
- conduct reproducible research 
- apply computational techniques
- work with big (and small) data 
- solve practical problems 
- communicate findings to varied audiences

]

.pull-right[
<img src="img/learning-labs.png" height="350px"/>
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]

.panel[.panel-name[Guest Speakers]

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Discuss topics addressing both: 
- **Disciplinary** issues and concepts that distinguish LA from other research and teaching fields

- **Methodological** overviews and applications in their own research and practice

]

.pull-right[
<img src="img/laser-keynote.png" height="300px"/>
.font80[.center[[2021 LASER Keynote](https://youtu.be/gKM3T_CzC10)]]
]

]

.panel[.panel-name[Affinity Groups]

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Foster collaborations around shared goals, like: 
- Advanced Technologies
- Engagement Online
- Assessment and LA
- Social and Affective Domains  
- Intersection of DEI and LA
]

.pull-right[
<img src="img/affinity-groups.png" height="350px"/>
]
]

.panel[.panel-name[Online Community]

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Provide follow-up through:

- Facilitated discussions 
- Zoom webinars & workshops
- Peer review activities
- Resource repository 
]

.pull-right[<img style="float: left; padding: 25px;" src="img/scholarslack.png" height="400px"/>]

]

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.center[ ## .font130[.navy[.center[**Interactive Demo!**]]]

https://go.ncsu.edu/pprs-demo
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# Part 2: The LASER Insitute 2022.0

Lessons Learned, Instructional Shifts, and Discussion

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# Lessons Learned

.panelset[

.panel[.panel-name[Disciplinary Knowledge]

.pull-left[
## All Aboard

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.pull-right[
## Digging Deeper
*“...it would have been helpful to have to select one ‘track’ so we could have **opportunities to really deepen our understanding of one method** and get some additional hands-on time with it.”*
]

]

.panel[.panel-name[Tech(nical) Skills]

.pull-left[
## impRoved proficiency
- *"**I understand R more closely now** and the myriad functions and features that can be used to wrangle, clean, and visualize data."*

- *“**The labs were critical** in developing my proficiency, particularly with the guidance of an instructor present."*
]

.pull-right[
## Too Much Tech

]
]

.panel[.panel-name[Social Capital]
.pull-left[
## A Good Start 
*“Just by **working with others in the labs and having discussions in the affinity groups was very helpful.** I've gained some LinkedIn and Twitter connections and have looked forward to that."*

]

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## Sustaining Connections
*“I think regular check-ins and additional workshops throughout the year will be very helpful... **organize meetups or invite scholars to virtual events** over the next year and collaboration will continue to grow stronger.”
”*
]

]

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# Diving Deeper

.panelset[

.panel[.panel-name[Learning Labs]
Each Content Area (e.g. Data Viz, Text Mining, Machine Learning) will consist of four progressively sequenced Learning Labs that include: 
- **Intro Workshop** to summarize core concepts and practice key R skills
- **Essential reading(s)** and discussions that unpack core concepts
- **Guided practice** with key R skills consisting of a [Case Study](https://laser-institute.github.io/aera-workshop/pages/text-mining-demo.html) (ML, TM, and SNA) or [tutorial](https://rstudio.cloud/learn/primers) (FLA, VIZ)
- **Independent analysis** that applies the concepts/skills to a dataset provided or their own 
- A **badge/microcredential** that demonstrates their competency

]

.panel[.panel-name[Guest Speakers]

Our Guest Speaker series will feature authors of the recently updated [Handbook of Learning Analytics](https://www.solaresearch.org/publications/hla-22/) presented by their author(s) presenting topics such as: 
- What is learning analytics?
- Learning Analytics for Understanding and Supporting Collaboration
- Data Literacy and Learning Analytics
- Learning Analytics and Learning at Scale
- Fairness, Absence of Bias, and Equity in Learning Analytics

]

.panel[.panel-name[Community Building]

We've partnered with [Participate, Inc.](https://www.participate.com) to help us: 
- develop in-person and online **community-building activities**
- organize **affinity groups** for collaborative research 
- structure monthly **progress updates** with scholars
- facilitate online **discussions forums**

]

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.center[ ## .font130[.navy[.center[**Summer Workshop Discussion**]]]

https://go.ncsu.edu/laser-schedule
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## .font130[.center[**Thank you!**]]

.center[<img style="border-radius:50%;" src="img/shaun.jpeg" height="200px"/><br/>**Shaun Kellogg**<br/><mailto:sbkellog@ncsu.edu>]

.pull-left-narrow[.center[<img style="border-radius: 50%;" src="https://www.nsf.gov/images/logos/NSF_4-Color_bitmap_Logo.png" height="150px"/> ]]

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.left[.font70[

This work was supported by the National Science Foundation grant DRL-2025090 (ECR:BCSER). Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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