Release of Connected Scholar Mobile Application – is an evidence-based training program

June 4, 2020

This project will create a new app/integrated website called the Connected Scholars App (CS app) that combines and extends the reach and capabilities of two successful products: the Connected Scholars (CS) program and an interview app (existing app). CS is an evidence-based training program developed to advance first-generation high school and college students’ help-seeking, educational success, and professional development. The CS app will extend the capabilities of the existing app by integrating the CS curriculum and adding advanced technical capabilities. CS has been successfully piloted in both high school and college classrooms. The existing app was developed by Hubspire in conjunction with the Library of Congress’ Veterans History Project (VHP) and the UCLA Graduate School of Education. The project director will work closely with the creators of CS and other content and technical partners. There are three stages of development: creating a prototype and conducting a beta test; expanding the prototype into a commercially-viable product and performing scaled testing, and expanding the CS curriculum and assessment material to more fully capitalize on the e-learning features. The grant funding will be used for the initial stage. The CS app is designed to be used for online learning and interaction via computers and mobile devices.

This project extends the reach and capabilities of the Connected Scholars (CS) program, an evidence-based curriculum developed to advance first generation high school and college students’ help-seeking, educational success, and professional development. CS has been piloted as an in-person course with over 140 participants, including high school students, incoming college students, and college freshmen and sophomores. We have conducted both quantitative and qualitative studies of CS. Most recently we found that, relative to a comparison group of UMB students, those who participated in CS reported better relationships with faculty, reduced avoidance of help-seeking, and, according to their academic records, a higher GPA one year later (Schwartz et al., 2017). Variations of this model, using youth-initiated mentoring, have been rigorously evaluated and shown to lead to improvements in academic and career outcomes, including reduced attrition. And, importantly, mentoring relationships are more enduring when students play a greater role in selecting their own mentors (Schwartz, Rhodes, Spencer, & Grossman, 2013).

One of the most relevant success stories here come from our clients within the education sector.

Some of the key challenges they faced were:

  • Inability to collect or process large amounts of complex datasets.
  • Limitations around effectively preparing students for long-term career and workforce success.
  • Lack of data-backed intelligent learning solutions that were available to young graduates, especially marginalized youth populations.
  • Demand for theoretically-informed, affordable, and scalable solutions that educational institutions can use to connect their graduates with domain experts or successful professionals and help them build their social capital.

Our approach to addressing these challenges lay the groundwork for translating evidence-based practices into the design and iterative development of a new mobile app to prepare youth for employment as well as inform our understanding of the role that social networks play in youth pathways into the workplace

Hubspire developed an innovative solution that leveraged Multimodal Learning Analytics (MMLA) methods to allow for more naturalistic and context-sensitive ways to assess personal characteristics and soft skills. The mobile application captured and processed rich modalities of communication, such as speech and nonverbal interaction (e.g., movements, gestures, facial expressions, gaze, biometrics, etc.) during recorded interview sessions. The application backend analyzed interview videos and speech using AI algorithms like tone analyzer and sentiment analysis to deliver intelligent insights as powerful dashboard visualizations. The solution allowed mentors to highlight and tag areas of concern or interest within interviews, with an option of leaving comments for improvement.

The solution was designed with a low-friction workflow to enable a seamless mentoring program for both end-users and the mentor. Mentorship lifecycle from start to finish could also be managed within the mobile application including: registration, pairing, scheduling, conversation guidance. One of the key highlights of the solution was ease of implementation and its ability to scale.