If you do a Google search for “Role Models”, you get 5 million results. “Mentor” yields 2.6 million results, “STEM mentors” 1.6 million and “Apprenticeship” 2.6 million.
These words (and concepts) are powerful and relevant as we see a big burst of public interest. We put together a graphic in an attempt to make sense of the mentoring organizations (particularly in STEM) that we bump into frequently. We wanted to compile best practices, lessons learned, latest research stats from these organizations and tease out patterns and recommendations for the field. The graphic below is one of the lenses we used.
|Mentoring organizations that are active today.
Most of the organizations fall within the past 30 years. Organizations such as Girls Inc., began as Girls Clubs of America in 1945, changed structure and evolved over time to respond better to the changing needs of youth.
Today there is also a rise in efforts to aggregate and share mentoring opportunities across the country, such as with the Million Women Mentors initiative and US2020. But the most prominent 20th century pioneers in the mentor landscape is Big Brothers and Big Sisters. Before 1900, mentoring was just called something different. It was called apprenticeship.
The biggest difference between apprenticeship then and now is that traditional “Michelangelo”-style apprenticeships taught skills that were visible (such as sculpting, painting, Taekwondo or any other martial art form, woodworking). The process of carrying out the task was visible both to the apprentice and master, for observation, comment, refinement, and correction. [Collins et al 1987].
Today, the apprenticeship model has evolved to teach the cognitive processes experts use to handle complex tasks. In the “cognitive apprenticeship” model experts/mentors impart both factual and conceptual knowledge in a variety of contexts, encouraging both a deeper understanding of the meaning of the concepts and facts themselves and a rich web of memorable associations between them and problem solving contexts. It is this dual focus on expert processes and situated learning that makes mentoring such a valuable solution for educational problems of “brittle skills and inert knowledge”.
A lodestar in this area has been the research by Allan Collins, John Seely Brown and Susan E. Newman (1987). They identified the following characteristics of an ideal learning environment and surprisingly – by introducing mentors – it is possible to create just such an ideal learning environment – at scale.
While most mentoring organizations share these learning environment characteristics, we have profiled at least one for each to provide an example of how this framework is being currently practiced in the field.
Domain knowledge – conceptual and factual knowledge generally found in school textbooks, class lectures, and demonstrations.
- We Teach Science – A remote tutoring and mentoring program that connects professionals with students, who tutor them in math subjects during school time
Heuristic strategies – are generally effective techniques and approaches for accomplishing tasks that might be regarded as “tricks of the trade'”.
- Intel Computer Clubhouse – leverages mentor knowledge and support to teach young people how to use modern technology and software in applicable and adaptable ways. By learning in a flexible, non-rigid environment, youth learn that there is more than one way to solve a problem.
Control strategies – As students acquire more heuristics for solving problems, they encounter a new management or control problem: how to select among the various possible problem-solving strategies, how to decide when to change strategies, and so on. For instance, a strategy for solving a complex problem might be to switch to a new part of a problem, if one is stuck on another part.
- US FIRST, ACE – STEM professionals participate on a team with students to create a novel design. This group project model provides real world experience, where mentors expose students to ‘tricks of the trade’ as well as control strategies.
Modeling, Coaching, Scaffolding and Fading – are the core of cognitive apprenticeship and help students acquire cognitive and metacognitive skills through observation and guided practice.
Articulation and Reflection – methods designed to help students both focus their observations of expert problem solving and gain conscious access to (and control of) their own problem-solving strategies.
- The Curiosity Machine online learning platform provides children with the curriculum to do engineering projects and connects them to one-on-one mentors who give them direct feedback on how to improve their projects. Mentors support children to reflect on the process of learning and acquire metacognitive skills.
- iCouldBe – Mentees engage online in structured curricular activities to learn, explore, research and reflect on their academic and personal challenges, set goals, identify resources and put an action plan in place, supported by mentors at every stage.
Exploration – The final method (exploration) is aimed at encouraging learner autonomy, not only in carrying out expert problem solving processes, but also in defining or formulating the problems to be solved.
- Intel Computer Clubhouse – students explore new technology in a supportive learning environment. As they build skills, they are able to define their own challenges and lead their own design projects
- Technovation Challenge teaches girls how to create mobile apps to solve a problem in their community and to launch it as a business.
- Techbridge – encourages girls to brainstorm, design, and redesign projects and use technology and engineering skills in the process.
Increasing complexity – refers to the construction of a sequence of tasks and task environments or microworlds where more and more of the skills and concepts necessary for expert performance are required
- iCouldBe – Mentors guide mentees through a structured sequential online curriculum built on an architecture of Missions/Quests/Activities with increasingly challenging tasks.
Increasing diversity – refers to the construction of a sequence of tasks in which a wider and wider variety of strategies or skills are required.
- Techbridge – curriculum introduces scientific concepts and engineering design principles building girls’ content knowledge, confidence, leadership, and skills throughout the length of the year long curriculum.
- Curiosity Machine’s badge-based system allows students to gain technical skills and advance from simple to more complex engineering designs
Global before local skills – Students learn to build a conceptual map, before attending to the details of the terrain. For instance, in a tailoring apprenticeship, apprentices learn to put together a garment from precut pieces before learning to draw and cut out the pieces themselves.
- Technovation Challenge starts with girls exploring the realm of mobile app possibilities, allowing them to build a conceptual map of options before learning to actually program the app.
Situated learning – A critical element in fostering learning is to have students carry out tasks and solve problems in an environment that reflects the multiple uses to which their knowledge will be put in the future.
- Big Brothers Big Sisters – one-to-one mentoring relationship allows student to explore a variety of social and academic environments, building skills appropriate for each setting
- iCouldBe – Throughout the curriculum mentors help mentees accomplish tasks and problem-solve across academic and personal themes and apply their learning to explore future career and college goals and opportunities
Culture of expert practice – refers to the creation of a learning environment in which the participants actively communicate about and engage in the skills involved in expertise, where expertise is understood as the practice of solving problems and carrying out tasks in a domain.
- US FIRST, Technovation, Curiosity Machine– In-person robotics competition and online learning platform both support an environment in which students engage with experts in STEM fields. Mentors reinforce how math and science are used in activities outside of the classroom
Leveraging cooperation – refers to having students work together in a way that fosters cooperative problem solving. Learning through cooperative problem solving is both a powerful motivator and a powerful mechanism for extending learning resources.
- US FIRST, Technovation, ACE – teams of students and STEM professionals work together over the course of the year to create and pitch a design
- Intel Computer Clubhouse – Using the internal global social network the Clubhouse Village, members collaborate with others of diverse ages, cultures, genders, and backgrounds, and gain new perspectives for understanding the world and themselves. Global youth leadership conferences such as the Teen Summit are also held, bringing members from 20 countries together to collaborate and learn from one another.
- Techbridge – girls work with other girls and role models in cooperative brainstorming and problem solving. From icebreakers, through hands-on activities, and reflections teamwork and cooperative skills are reinforced.
Leveraging competition – refers to the strategy of giving students the same task to carry out and then comparing what each produces. One of the important effects of comparison is that it provides a focus for students’ attention and efforts for improvement by revealing the sources of strengths and weaknesses . However, for competition to be effective, comparisons must be made not between the products of student problem solving, but between the processes.
- US FIRST, Technovation – based on a challenge model, teams of students and STEM professionals compete to create and pitch a design.
The above framework is just one way to look at the exciting landscape of mentoring organizations. But it does explain why a combination of digital technology and mentors can be such a powerful solution to today’s educational problems.
Mentoring organizations today are pushing the boundaries of technology (through various forms of virtual mentoring) to maximize the mentor’s time and expertise and craft ideal learning environments.
“Perhaps less obviously, we believe that the core techniques of modelling, coaching and fading can be formalized and embedded in tomorrow’s powerful personal computers, thereby fostering a renewal of apprenticeship-style learning in our schools.”
“We believe the thrust toward computer-aided learning is an important development in education for several reasons. First, computers make it possible to give more personal attention to individual students, without which the coaching and scaffolding of apprenticeship-style learning are impossible.”
“Appropriately designed computer-based modelling, coaching, and fading systems can make cost-effective and widely available a style of learning that was previously severely limited. Of course, apprenticeship-based computer systems need not take on the total responsibility. Instead, they only need to augment the master teacher in a way that amplifies and makes her efforts more cost-effective.” [Collins et al 1987]
(Graphic created by Audra Torres. Data gathered through interviews with senior leadership at organizations whenever possible. Interviews were conducted by Andrew Collins, Mentor Community Manager at Iridescent. This article is the first in a series of three. The following articles will look more closely at the depth and type of impact each organization is having as well as organizations focused on engaging girls).
Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics. Technical Report No. 403. By AllanCollins, BBN Laboratories, John Seely Brown, Susan E. Newman, Xerox, Palo Alto Research Center, January 1987
This article has also been published on LinkedIn Pulse and the Huffington Post.