This was a working group Fall 2017 - Fall 2018 and is no longer active.

Are you interested in developing quantitative and qualitative research skills as a researcher on inclusive teaching?

Directed by Andrew Estrada Phuong and Judy Nguyen, the Design for Equity Lab (D4E Lab) examines how adaptive learning environments and organizational practices can enhance equity. The D4E Lab employs quantitative and qualitative research methods to address a variety of research questions regarding equity, access, and inclusion.

In the D4E Lab working group, you will engage in quantitative and qualitative data analysis. You will learn how to use statistical and data visualization software, such as Stata. Using randomized control trials, we’re studying how adaptive equity-oriented pedagogies and non-adaptive pedagogies impact the following outcomes in higher education:

• student achievement

• stereotype threat

• psychosocial outcomes (e.g., motivation, sense of self-efficacy, sense of belonging, etc.)

• growth mindsets

• academic trajectories

• persistence

Controlling for multiple intersectional identities, our goal is to identify high-impact teaching practices that benefit all students, especially those from low-income, underrepresented, and marginalized backgrounds. This research will inform innovations in teaching and instructor professional development on campus and beyond!

If you would like more information, please email aphuong@berkeley.edu . Some of the D4E Lab’s core research areas include teaching and learning, organizational learning, faculty development, and student success programs.

Here’s an example publication of our work that is hyperlinked below:

Phuong, A. E., Nguyen, J. & Marie, D. (2017b). Evaluating an adaptive equity-oriented pedagogy: A study of its impacts in higher education. The Journal of Effective Teaching. 17(2), 5-44.

**On hiatus Spring 2019**

Are you new to machine learning but do not know how to get started? Do you have experience with machine learning and are looking for a venue to practice presenting your research? Would you like to lead a coding walkthrough? If you answered yes to either of these questions, then come join the UC Berkeley D-Lab Machine Learning Working Group! 

This brown-bag series will introduce you to central themes in the form of workshop-style coding walkthroughs using R and Python.

In Fall 2018 the focus will be on unsupervised methods:

  - September 5: Principal Component Analysis   

  - September 19: k-means clustering  

  - October 3: hierarchical clustering  

  - October 17: Medoid partitioning  

  - October 31: tSNE  

  - November 14: UMAP  

  - November 28: TextXD / Latent Class Analysis (?)  

  - December 12: Lightning talks  

We will focus on key frameworks in R such as caret and SuperLearner and in Python like scikit-learn, tensorflow, and keras. 

We also encourage you to bring topics for discussion that focus on a variety of themes including data cleaning, visualization, automation, cloud computing, and parallel processing. 

Prior knowledge: R FUN!damentals: Parts 1 through 3 or previous intermediate working knowledge of R or Python FUN!damentals and previous work with NumPy and SciPy are recommended. 

Click here download install R

Click here to download RStudio Desktop Open Source License FREE

Click here to download Python Anaconda Distribution

Visit our GitHub repo at: https://github.com/dlab-berkeley/MachineLearningWG

 This was a working group Fall 2017 - Fall 2018 and is no longer active.

Are you interested in developing quantitative and qualitative research skills as a researcher on inclusive teaching?

Directed by Andrew Estrada Phuong and Judy Nguyen, the Design for Equity Lab (D4E Lab) examines how adaptive learning environments and organizational practices can enhance equity. The D4E Lab employs quantitative and qualitative research methods to address a variety of research questions regarding equity, access, and inclusion.

In the D4E Lab working group, you will engage in quantitative and qualitative data analysis. You will learn how to use statistical and data visualization software, such as Stata. Using randomized control trials, we’re studying how adaptive equity-oriented pedagogies and non-adaptive pedagogies impact the following outcomes in higher education:

• student achievement

• stereotype threat

• psychosocial outcomes (e.g., motivation, sense of self-efficacy, sense of belonging, etc.)

• growth mindsets

• academic trajectories

• persistence

Controlling for multiple intersectional identities, our goal is to identify high-impact teaching practices that benefit all students, especially those from low-income, underrepresented, and marginalized backgrounds. This research will inform innovations in teaching and instructor professional development on campus and beyond!

If you would like more information, please email aphuong@berkeley.edu . Some of the D4E Lab’s core research areas include teaching and learning, organizational learning, faculty development, and student success programs.

Here’s an example publication of our work that is hyperlinked below:

Phuong, A. E., Nguyen, J. & Marie, D. (2017b). Evaluating an adaptive equity-oriented pedagogy: A study of its impacts in higher education. The Journal of Effective Teaching. 17(2), 5-44.

 This was a working group Fall 2017 - Fall 2018 and is no longer active.

Are you interested in developing quantitative and qualitative research skills as a researcher on inclusive teaching?

Directed by Andrew Estrada Phuong and Judy Nguyen, the Design for Equity Lab (D4E Lab) examines how adaptive learning environments and organizational practices can enhance equity. The D4E Lab employs quantitative and qualitative research methods to address a variety of research questions regarding equity, access, and inclusion.

In the D4E Lab working group, you will engage in quantitative and qualitative data analysis. You will learn how to use statistical and data visualization software, such as Stata. Using randomized control trials, we’re studying how adaptive equity-oriented pedagogies and non-adaptive pedagogies impact the following outcomes in higher education:

• student achievement

• stereotype threat

• psychosocial outcomes (e.g., motivation, sense of self-efficacy, sense of belonging, etc.)

• growth mindsets

• academic trajectories

• persistence

Controlling for multiple intersectional identities, our goal is to identify high-impact teaching practices that benefit all students, especially those from low-income, underrepresented, and marginalized backgrounds. This research will inform innovations in teaching and instructor professional development on campus and beyond!

If you would like more information, please email aphuong@berkeley.edu . Some of the D4E Lab’s core research areas include teaching and learning, organizational learning, faculty development, and student success programs.

Here’s an example publication of our work that is hyperlinked below:

Phuong, A. E., Nguyen, J. & Marie, D. (2017b). Evaluating an adaptive equity-oriented pedagogy: A study of its impacts in higher education. The Journal of Effective Teaching. 17(2), 5-44.

**On hiatus Spring 2019**

Are you new to machine learning but do not know how to get started? Do you have experience with machine learning and are looking for a venue to practice presenting your research? Would you like to lead a coding walkthrough? If you answered yes to either of these questions, then come join the UC Berkeley D-Lab Machine Learning Working Group! 

This brown-bag series will introduce you to central themes in the form of workshop-style coding walkthroughs using R and Python.

In Fall 2018 the focus will be on unsupervised methods:

  - September 5: Principal Component Analysis   

  - September 19: k-means clustering  

  - October 3: hierarchical clustering  

  - October 17: Medoid partitioning  

  - October 31: tSNE  

  - November 14: UMAP  

  - November 28: TextXD / Latent Class Analysis (?)  

  - December 12: Lightning talks  

We will focus on key frameworks in R such as caret and SuperLearner and in Python like scikit-learn, tensorflow, and keras. 

We also encourage you to bring topics for discussion that focus on a variety of themes including data cleaning, visualization, automation, cloud computing, and parallel processing. 

Prior knowledge: R FUN!damentals: Parts 1 through 3 or previous intermediate working knowledge of R or Python FUN!damentals and previous work with NumPy and SciPy are recommended. 

Click here download install R

Click here to download RStudio Desktop Open Source License FREE

Click here to download Python Anaconda Distribution

Visit our GitHub repo at: https://github.com/dlab-berkeley/MachineLearningWG

 This was a working group Fall 2017 - Fall 2018 and is no longer active.

Are you interested in developing quantitative and qualitative research skills as a researcher on inclusive teaching?

Directed by Andrew Estrada Phuong and Judy Nguyen, the Design for Equity Lab (D4E Lab) examines how adaptive learning environments and organizational practices can enhance equity. The D4E Lab employs quantitative and qualitative research methods to address a variety of research questions regarding equity, access, and inclusion.

In the D4E Lab working group, you will engage in quantitative and qualitative data analysis. You will learn how to use statistical and data visualization software, such as Stata. Using randomized control trials, we’re studying how adaptive equity-oriented pedagogies and non-adaptive pedagogies impact the following outcomes in higher education:

• student achievement

• stereotype threat

• psychosocial outcomes (e.g., motivation, sense of self-efficacy, sense of belonging, etc.)

• growth mindsets

• academic trajectories

• persistence

Controlling for multiple intersectional identities, our goal is to identify high-impact teaching practices that benefit all students, especially those from low-income, underrepresented, and marginalized backgrounds. This research will inform innovations in teaching and instructor professional development on campus and beyond!

If you would like more information, please email aphuong@berkeley.edu . Some of the D4E Lab’s core research areas include teaching and learning, organizational learning, faculty development, and student success programs.

Here’s an example publication of our work that is hyperlinked below:

Phuong, A. E., Nguyen, J. & Marie, D. (2017b). Evaluating an adaptive equity-oriented pedagogy: A study of its impacts in higher education. The Journal of Effective Teaching. 17(2), 5-44.

 This was a working group Fall 2017 - Fall 2018 and is no longer active.

Are you interested in developing quantitative and qualitative research skills as a researcher on inclusive teaching?

Directed by Andrew Estrada Phuong and Judy Nguyen, the Design for Equity Lab (D4E Lab) examines how adaptive learning environments and organizational practices can enhance equity. The D4E Lab employs quantitative and qualitative research methods to address a variety of research questions regarding equity, access, and inclusion.

In the D4E Lab working group, you will engage in quantitative and qualitative data analysis. You will learn how to use statistical and data visualization software, such as Stata. Using randomized control trials, we’re studying how adaptive equity-oriented pedagogies and non-adaptive pedagogies impact the following outcomes in higher education:

• student achievement

• stereotype threat

• psychosocial outcomes (e.g., motivation, sense of self-efficacy, sense of belonging, etc.)

• growth mindsets

• academic trajectories

• persistence

Controlling for multiple intersectional identities, our goal is to identify high-impact teaching practices that benefit all students, especially those from low-income, underrepresented, and marginalized backgrounds. This research will inform innovations in teaching and instructor professional development on campus and beyond!

If you would like more information, please email aphuong@berkeley.edu . Some of the D4E Lab’s core research areas include teaching and learning, organizational learning, faculty development, and student success programs.

Here’s an example publication of our work that is hyperlinked below:

Phuong, A. E., Nguyen, J. & Marie, D. (2017b). Evaluating an adaptive equity-oriented pedagogy: A study of its impacts in higher education. The Journal of Effective Teaching. 17(2), 5-44.

**On hiatus Spring 2019**

Are you new to machine learning but do not know how to get started? Do you have experience with machine learning and are looking for a venue to practice presenting your research? Would you like to lead a coding walkthrough? If you answered yes to either of these questions, then come join the UC Berkeley D-Lab Machine Learning Working Group! 

This brown-bag series will introduce you to central themes in the form of workshop-style coding walkthroughs using R and Python.

In Fall 2018 the focus will be on unsupervised methods:

  - September 5: Principal Component Analysis   

  - September 19: k-means clustering  

  - October 3: hierarchical clustering  

  - October 17: Medoid partitioning  

  - October 31: tSNE  

  - November 14: UMAP  

  - November 28: TextXD / Latent Class Analysis (?)  

  - December 12: Lightning talks  

We will focus on key frameworks in R such as caret and SuperLearner and in Python like scikit-learn, tensorflow, and keras. 

We also encourage you to bring topics for discussion that focus on a variety of themes including data cleaning, visualization, automation, cloud computing, and parallel processing. 

Prior knowledge: R FUN!damentals: Parts 1 through 3 or previous intermediate working knowledge of R or Python FUN!damentals and previous work with NumPy and SciPy are recommended. 

Click here download install R

Click here to download RStudio Desktop Open Source License FREE

Click here to download Python Anaconda Distribution

Visit our GitHub repo at: https://github.com/dlab-berkeley/MachineLearningWG

 This was a working group Fall 2017 - Fall 2018 and is no longer active.

Are you interested in developing quantitative and qualitative research skills as a researcher on inclusive teaching?

Directed by Andrew Estrada Phuong and Judy Nguyen, the Design for Equity Lab (D4E Lab) examines how adaptive learning environments and organizational practices can enhance equity. The D4E Lab employs quantitative and qualitative research methods to address a variety of research questions regarding equity, access, and inclusion.

In the D4E Lab working group, you will engage in quantitative and qualitative data analysis. You will learn how to use statistical and data visualization software, such as Stata. Using randomized control trials, we’re studying how adaptive equity-oriented pedagogies and non-adaptive pedagogies impact the following outcomes in higher education:

• student achievement

• stereotype threat

• psychosocial outcomes (e.g., motivation, sense of self-efficacy, sense of belonging, etc.)

• growth mindsets

• academic trajectories

• persistence

Controlling for multiple intersectional identities, our goal is to identify high-impact teaching practices that benefit all students, especially those from low-income, underrepresented, and marginalized backgrounds. This research will inform innovations in teaching and instructor professional development on campus and beyond!

If you would like more information, please email aphuong@berkeley.edu . Some of the D4E Lab’s core research areas include teaching and learning, organizational learning, faculty development, and student success programs.

Here’s an example publication of our work that is hyperlinked below:

Phuong, A. E., Nguyen, J. & Marie, D. (2017b). Evaluating an adaptive equity-oriented pedagogy: A study of its impacts in higher education. The Journal of Effective Teaching. 17(2), 5-44.