Assumed But Not Proven Examining the Efficacy of e-Mentoring within The Phoenix Scholars

Devon Cash

ABSTRACTe-Mentoring, or electronic mentoring, allows mentors and mentees to communicate without being restricted by time and geography. As such, it is often assumed that these benefits make it superior to traditional, in-person mentoring. However, a closer look into the efficacy of the e-mentoring model within the context of The Phoenix Scholars, an education nonprofit serving disadvantaged youth, reveals that e-mentoring lacks the ability to sustain meaningful interactions between mentors and mentees.

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Assumed But Not ProvenIn today’s world, people have the power to phone someone from their computers

and video chat someone from their phones. The pace of technological advancement has radically changed the way individuals interact with the world around them. However, the brilliance of technological innovation should always be contextualized within its own usefulness and applicability. (Recall the wearable PC and the two-in-one portable gaming system and cell phone!) Oftentimes the benefits of a particular innovation are assumed but not proven. E-mentoring is an example of this, at least for The Phoenix Scholars, as the model has been utilized since the program’s inception without thought or evaluation…until now.

The Phoenix Scholars’ ProblemFounded in 2009, The Phoenix Scholars is a student-run, Stanford-based 501(c)

(3) nonprofit organization, which provides pro bono college counseling services to low income, first generation, and minority high school seniors in the state of California. The program pairs these seniors (scholars) with Stanford and Berkeley undergraduate mentors who help them apply to college, prepare for standardized tests, and obtain financial aid and scholarships. Ultimately, The Phoenix Scholars believes that though talent and intelligence are universal, opportunities and resources are not. As such, TPS is a source of such counseling opportunities and resources in a state where the student to counselor ratio is 945:1.1

Overall, the program has had major success. TPS has worked with over 450 undergraduate mentors, sent 500 scholars to four-year institutions (with 26% of the group attending select colleges and universities),2 and established a host of professional connections with other philanthropic organizations, including the Silicon Community Valley Foundation, one of the nation’s largest private foundations with over $6.5 billion in assets under management.3

Despite the organization’s successes, scholar retention has not been easy. For the 2015-2016 academic year, TPS accepted a cohort of 161 scholars and 93 mentors. From the start of the program, which began in the June following the scholars’ junior year of high school, scholars were assigned monthly tasks and were encouraged to keep in contact with their mentors on a bi-weekly basis. By October 2015, five months into the year’s cycle, there was a steep decline in scholar responsiveness. TPS’ executive team identified the most unresponsive scholars and gave them an ultimatum demanding that they reengage in the program or be asked to leave. At the end of the “intervention,” 19 of the 161 scholars (11.80%) left the program.

If the organization is to grow, understanding why these scholars left is extremely important. Such information will allow the program to improve its weak areas and emphasize its strong areas.

The e-Mentoring Problem Because TPS is based in Stanford University, where nearby Palo Alto had a

median household income of $126,771 for the period from 2010 to 2014,4 recruiting students from underprivileged communities requires that the organization travel great distances to reach its target demographic. The geographic breakdown of this year’s cohort is as follows:

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Alameda31%

Contra Costa1%

Los Angeles45%Merced

4%

Orange1%

Sacramento1%

San Bernardino1%

San Francisco1%

San Joaquin4%

San Mateo3%

Santa Clara6%

Solano 1%

Percentage of Scholars by County

Figure 1 shows the percentage of scholars living in various California counties.5

The heat map provides another look at the geographic distribution of the 2015-2016 cohort:

Figure 2 shows a heat map representing various concentrations of scholars throughout California. Purple dots indicate that scholars are present in the region. Yellowish dots are areas of high scholar concentration. The red arrow is directed at Alameda County. The yellow arrow is directed at Los Angeles County. Visit https://www.myheatmap.com/app/map/18137 to be directed to the interactive version of the map.

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Notice that Los Angeles County, where 45% of scholars are located, is approximately 360 miles away from Stanford’s campus.6 Alameda County, another hub for scholars, is approximately 50 miles away.7 Because of the great spatial distance between scholars and mentors, e-mentoring, or electronic mentoring, has been the primary facilitator of mentor-scholar interactions.

Though e-mentoring is a convenient, cost-effective model for the organization, it obviates the need for in-person communication between mentors and scholars, perhaps causing scholars to be disinterested in the program. As such, it can be conjectured that the decline in scholar participation the organization experienced in October 2015 is due in large part to the impersonal nature of e-mentoring.

Existing Research There are differing perspectives among academics on the efficacy of e-mentoring in

helping mentees acquire an enhanced set of skills or knowledge. Virtually all scholars agree that e-mentoring, as is the case with technology in general, allows for communication unrestricted by time and geography.8–11 This quality of e-mentoring gives mentees nearly unlimited access to the world around them. Through a few keystrokes, mentees can be connected with government officials, business people, scientists, and artists at any time from any place. Beyond that, however, there are compelling arguments in either direction that make issuing judgment on the efficacy of e-mentoring very difficult. Such arguments are extremely layered. The most common debates, however, revolve around the following questions:

What are the expectations of the e-mentoring relationship? What are the primary pros and cons associated with synchronous and

asynchronous communication? Which mentees benefit the most from e-mentoring?

Addressing these questions will help summarize the existing research on e-mentoring. Then, by contextualizing each of these questions within The Phoenix Scholars’ philosophy, it will be clear whether or not the existing research lends itself to determining if the e-mentoring model is a good one for the organization.

What are the expectations of the e-mentoring relationship?Specifically, this question suggests that e-mentoring may not be effective if

mentors and mentees do not have the same understanding of what they will be receiving from the mentorship experience. Many academics assert that a deep, personal connection based on mutual trust between mentors and mentees is essential in building a successful e-mentoring relationship.8,9 Such a connection requires that the e-mentoring relationship transcend mere information sharing, by which expertise or knowledge is exchanged.11 E-mentoring, at its core, should be a form of psychoemotional support, by which issues related and unrelated to the mentoring subject can be addressed. When that element of personal support is lacking, however, mentoring relationships tend to be nothing more than “weak ties,” which either break easily or wither eventually.9

However, in their study of e-mentoring conducted at the University of Limerick (UL) in Ireland, Risquez and Sanchez-Garcia found that on a few occasions, mentees had no expectation of developing personal relationships with their mentors. In the study, Risquez and Sanchez-Garcia looked at the ways in which computer-mediated

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communication (CMC)—an umbrella term for all digital communication including e-mentoring—could be used in the mentoring context to help in students’ transition to tertiary study (i.e., the secondary school to college transition). Using a sample of 81 mentees and 42 mentors, Risquez and Sanchez-Garcia implemented an evaluation of UL’s three to twelve-month e-mentoring program. Ultimately, it was found that quite often mentees expected their mentors to be sources of academic information, not necessarily personal coaches. Even more interesting, some of the more introverted mentees appreciated that e-mentoring provided them with a safe space to avoid building personal connections with their mentors. More generally, this case study suggests that the expectations of e-mentoring are subject to the experiences of those involved, specifically the mentees. As such, it may be too bold to assert that a certain level of emotional engagement is a requirement for beneficial e-mentoring relationships.

As it relates to The Phoenix Scholars, this research suggests that perhaps scholars do not expect to build personal connections with their mentors and the program as a whole in the first place. More specifically, perhaps the goal of e-mentoring should be focused strictly on the mentoring subject matter (i.e. the college application process) and not on personal relationship building. However, this logic seems counterintuitive, especially within the context of The Phoenix Scholars. Unlike UL, TPS aims to work only with students from extremely marginalized populations. Though a disengaged mentee-mentor relationship may be successful in circumstances where social indicators, such as race, socioeconomic status, and level of educational attainment, are not at the foreground, such a relationship is contrary to the needs of TPS’ students and the philosophy of the organization as a whole.

What are the primary pros and cons associated with synchronous and asynchronous communication?

Not all e-mentoring is created equal. This question suggests that the type of e-mentoring implemented may be a key determinant in judging the efficacy of an e-mentoring model. There are two broad types of digital communication: synchronous and asynchronous. Synchronous communication allows mentors and mentees to communicate simultaneously (e.g., phone conversations and video chats), while asynchronous communication typically involves a delayed response (e.g., emails, texts, and instant messages).

Most of the debate about communication methods deals with asynchronous communication. This is probably because when e-mentoring does occur, it typically is facilitated by a portal service like Moodle, a software learning management system. Such systems allow an organization to control the environment in which e-mentoring occurs (as opposed to The Phoenix Scholars, where mentoring communications are decentralized and vary from pair to pair). Portals also make it easy to track and archive communications and ensure privacy while online. However, these portal services lack synchronous communication features. Thus, programs forfeit the option to have synchronous communication features in favor of greater logistical ease. As such, most of the resulting research on the e-mentoring initiatives of such programs revolves around the asynchronous elements of the portal services they utilize (e.g., forums, emails, instant messages, announcements, etc.). However, where there are opinions about synchronous

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communication, the consensus is that it is a viable imitation of face-to-face communication.10

On the topic of asynchronous communication, however, the debate is intense. There is the common opinion that asynchronous communication is too lexical to convey ideas accurately. Though emojis and case usage can help in aiding conversations, the absence of human cues (i.e., gestures and expressions) leaves room for miscommunication.8,9 Another critique of asynchronous communication, aligned with the idea that mentoring is supposed to be personal in nature, is that such communication makes brevity commonplace and substantive dialogue nearly impossible.8

In direct response to the latter critique, other arguments suggest that asynchronous communication actually leads to the development of “hyper-relationships.”9 Such relationships are characterized by a high level of comfort and security afforded by the lack of face-to-face interaction. Because of the delayed response aspect of such communication, participants in a conversation do not have to worry about being pressured to talk. Quite the opposite: participants in such conversations have the opportunity to craft very thoughtful and measured responses, which filter out unimportant information.

As it relates to The Phoenix Scholars, this research is vital. It adds another dimension to the conversation. Perhaps choosing a particular type of e-mentoring, either synchronous and asynchronous, may be what is needed as opposed to removing the e-mentoring model together. Maybe within the field of synchronous communication, video chats are more effective than telephone calls. Maybe within the field of asynchronous communication, email is a better medium for conveying information than texts. In any case, this research suggests that the diversity within e-mentoring may make it very difficult to condemn the model as a whole.

Which mentees benefit the most from e-mentoring?This questions suggests that the type of individuals involved in the mentoring

process plays a big role in whether or not the e-mentoring relationship will be a successful one. For example, can an affluent, white businesswoman from the Bible Belt effectively mentor a teenaged Latina girl from inner city Los Angeles? Some researchers suggest that the emotional separation afforded by e-mentoring makes such a question irrelevant. More specifically, digital platforms allow for relationships to develop without cultural, social, or personal baggage hindering the process.11

However, some say that such an argument misses the bigger picture altogether. This is to say that if it is better for a mentor-mentee pair to use e-mentoring to simply avoid misunderstanding or miscommunication, there is a good chance that that mentor-mentee pair should not have been established in the first place. Ultimately, the mentoring experience should enhance relationships, not abstract them.

The Phoenix Scholars makes it an aim to recruit a pool of mentors that either share similar backgrounds and experiences to the scholars or who have direct experience working with low income, first generation, and minority youth. Relating to the organization’s philosophy that the mentoring relationship should be centered on deep, personal connections, TPS is of the opinion that those relationships develop best when mentees have someone they can relate to and see part of themselves in.

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In general, the existing research allows the conversation surrounding e-mentoring to be had in many ways. A discussion of mentor and mentee expectations, asynchronous versus synchronous communication, and the similarity between mentors and their mentees adds a level of rigor to the question at hand regarding e-mentoring. Though this research primarily focuses on comparing e-mentoring with in-person mentoring, insights on all of these points help place this work within an existing dialogue.

Methodology The overall goal of this study was to gauge how engaged scholars were with their

mentors, what that engagement looked like, and how that level of engagement could be improved. I sent out a 10-minute survey to 102 scholars (i.e., those scholars assigned to Stanford mentors) with a one-week deadline and $25 prize drawing to encourage participation. At the end of the week, I recorded 39 responses (27.46% of the remaining cohort). The online survey consisted of the following questions:

How often do you communicate with your mentor?a. Weeklyb. Bi-weeklyc. Monthly d. Quarterly

What is the primary method of communication between you and your mentor? a. In-person meetingsb. Telephonec. Video chat d. Text and instant messaging

How well do know your mentor?a. Not at allb. Somewhat c. Appropriately d. Very

What things would help you get to know your mentor better? This final question was free response.

Results The results are roughly divided into four parts: the distribution of responses to each of the survey questions and their application to the cohort as a whole, the correlation between specific variables, a regression analysis, and, finally, a look into causal effects.

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I. Distributions

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ly = 1

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y = 2

_x00

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i-week

ly = 3

Weekly

= 4

05

10152025

5

21

10

3

How often do you communicate with your mentor?

Frequency of Communication (x)

Number of Responses

Figure 3 shows the distribution of responses to the first survey question. Qualitative answers were assigned numerical values on a scale from 1 to 4, with 1 representing the most infrequent communication and 4 representing the most frequent communication.

The survey results to this question were extremely surprising. The Phoenix Scholars encourages mentors and scholars to communicate on a bi-weekly basis. However, from the sample, it is apparent that communication typically happens monthly. To put this in perspective, the program only lasts 12 months, meaning that over the course of a year, mentors and scholars only interact 12 times. Even more surprising was the fact that five of the 39 respondents reported only communicating with their mentors once per quarter. Considering there are monthly tasks for scholars to complete with their mentors, it is virtually impossible to be able to accomplish those tasks by talking only four times for the entire duration of the program. These results immediately raised a red flag. Perhaps it is not e-mentoring that is the problem, but rather the frequency of communication. The correlation analysis to follow will shed more light on the relationship between frequency of communication and how well scholars know their mentors.

A 95% confidence interval for the frequency of communication mean will reveal the frequency of communication between scholars and their mentors across the entire cohort:

1. General formula for the confidence interval : x± t ∙sx

√n2.x (averageresponse )=2.2821 ; t (number of standard deviations )=tinv ( .05 ,38 )=2.0244 ; sx (sample standard deviation)=0.7930

3. ⇒confidence interval:2.2821 ± 2.0244 ∙ 0.7930√38

4. ⇒ [2.0217¿2.5425]

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More specifically, this result suggests with 95% confidence that the frequency of communication between scholars and mentors in the cohort as a whole is between bi-weekly (3) and monthly (2).

Texts and instant mes-sages = 1

Telephone calls = 2

Video chats = 3

In-person meetings = 4

05

1015202530

24

11

40

What is the primary method of communication be-tween you and your mentor?

Method of Communication (w)

Number of Responses

Figure 4 shows the distribution of responses to the second survey question. Qualitative answers were assigned numerical values on a scale from 1 to 4, with 1 representing the most asynchronous communication and 4 representing the most synchronous communication.

Echoing the geographic distribution presented in “The e-Mentoring Problem,” the results show that out of the sample of 39 respondents, not a single scholar met with their mentor in person. All of the communication was done electronically. Interestingly, asynchronous communication in the form of texts and instant messages was the primary method of communication between scholars and their mentors. Synchronous communication in the form of telephone calls and video chats were much less common.

A 95% confidence interval for the method of communication mean will reveal the method of communication utilized by scholars across the entire cohort:

1. General formula for the confidence interval :w ± t ∙sw

√n2. w=1.4872 ; t=tinv ( .05 ,38 )=2.0244 ; sw=0.6833

3. ⇒confidence interval:1.4872 ± 2.0244 ∙ 0.6833√38

4. ⇒ [1.2628¿1.7116 ]More specifically, this result suggests with 95% confidence that the method of communication will either be texts and instant messages (1) or telephone calls (2).

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Not at all = 1 Somewhat = 2

Appropriately = 3

Very = 402468

101214161820

3

14

18

4

How well do you know your mentor?

Level of Connectedness (y)

Number of Responses

Figure 5 shows the distribution of responses to the third survey question. Qualitative answers were assigned numerical values on a scale from 1 to 4, with 1 representing the lowest level of connectedness and 4 representing the highest level of connectedness.

To be in a position to quantify the analysis as much as possible, the need for a dependent variable was essential. The assumption was that how well scholars know their mentors (y) is most likely dependent on the frequency of communication between them and their mentors (x) and the primary method of communication utilized (w), the two independent variables. This assumption lays the groundwork for the correlation and regression analysis that follows.

A 95% confidence interval for the level of connectedness mean will reveal the level of connectedness between scholars and their mentors across the entire cohort:

1. General formula for the confidence interval : y ±t ∙sy

√n2. y=2.5897; t=tinv ( .05 ,38 )=2.0244 ; s y=0.7853

3. ⇒confidence interval:2.5897 ± 2.0244 ∙ 0.7853√38

4. ⇒ [2.3318¿2.847]More specifically, this result suggests with 95% confidence that the scholars in this year’s cohort know their mentor either somewhat (2) or appropriately (3).

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II. Correlation Analysis Correlation analysis will help in indicating possible relationships that exist

between the proposed independent variables (“x” being the frequency of communication and “w” being the method of communication) and the proposed dependent variable (“y” being the level of connectedness).

1 2 3 41

2

3

4

Frequency of Communication (x) vs. Level of Connectedness (y)

Frequency of Communication (x)

Level of Connected-

ness (y)

Figure 6 shows the scatter plot where the independent variable is "x," the frequency of communication, and the dependent variable is "y," the level of connectedness.

A cursory glance at the graph suggests that there is a positive correlation between the frequency of communication and level of connectedness. A more rigorous approach to finding the correlation between the two variables is as follows:

1. ρ ( x , y )= cov (x , y)sx s y

= xy−x ∙ ysx sy

2. xy=6.2564 ; x=2.2821; y=2.5897 ; sx=0.7930; s y=0.78533. ρ ( x , y )=.5564

A correlation of .5564 is considered moderate positive correlation,12 indicating that there may be a link between the frequency of communication and the level of connectedness.

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1 2 3 41

2

3

4

Method of Communication (w) vs. Level of Connectedness (y)

Method of Communication (w)

Level of Connected-

ness (y)

Figure 7 shows the scatter plot where the independent variable is "w," the method of communication, and the dependent variable is "y," the level of connectedness.

A glance at the graph shows a positive, but unconvincing, correlation between the method of communication and the level of connectedness. A rigorous analysis of the correlation provides the following result:

1. ρ (w , y )= cov (w , y)sw sy

=wy−w ∙ ysw s y

2. wy=4.051; w=1.4872; y=2.5897; sw=0.6833 ;s y=0.78533. ρ (w , y )=.3724A correlation of .3724 is considered weak,12 which does not really say much about the

relationship between the method of communication and the level of connectedness. Interestingly, because the correlation between “x” and “y” is greater than the

correlation between “w” and “y,” the statistics suggest that the frequency of communication has a greater impact on the level of connectedness than does the method of communication. Reliance on the statistics alone would in part invalidate the hypothesis that e-mentoring (i.e., the method of communication) contributed to the lack of relationships built within The Phoenix Scholars. However, correlation is not causality. A look at the fourth survey question later in the paper will help contextualize the significance of the statistics that were uncovered.

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III. Regression AnalysisThough there were no strong correlations between the variables, simple linear

regression analysis will provide a line that best estimates y-values given particular x-values and w-values.

1 2 3 41

2

3

4

f(x) = 0.565450643776824 x + 1.29935622317597R² = 0.326031169880072

Frequency of Communication (x) vs. Level of Connectedness (y)

Frequency of Communication (x)

Level of Connected-

ness (y)

Figure 8 shows the regression line that best estimates the data from the scatter plot "Frequency of Communication (x) vs. Level of Connectedness (y)."

From the graph, the regression line that best estimates the data is y = 0.5655x + 1.2994. Finding the frequency of communication that will maximize the level of connectedness requires the following process:

1. Set y = 4, i.e., assume that the goal is for scholars to know their mentors “very” well

2. Solve for “x,” which will be the frequency of communication that will maximize the level of connectedness

3. ⇒ x = 4.7756Because x = 4.7756, the regression line suggests that for scholars to know their mentors “very” well, they should communicate at least on a weekly basis.

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1 2 3 41

2

3

4

f(x) = 0.439306358381503 x + 1.9364161849711R² = 0.146115025107195

Method of Communication (w) vs. Level of Connectedness (y)

Method of Communication (w)

Level of Connected-

ness (y)

Figure 9 shows the regression line that best estimates the data from the scatter plot "Method of Communication (w) vs. Level of Connectedness (y)."

From the graph, the regression line that best estimates the data is y = 0.4393w + 1.9364. Finding the method of communication that will maximize the level of connectedness requires the following process:

1. Set y = 4, i.e., assume that the goal is for scholars to know their mentors “very” well

2. Solve for “w,” which will be the method of communication that will maximize the level of connectedness

3. ⇒ w = 4.6975Because x = 4.6975, the regression line suggests that for scholars to know their mentors “very” well, they should communicate in-person.

In general, the regression line, though not based on the strongest data, predicts the most common sense solutions. In short, the statistics conclude that for scholars to know their mentors “very” well, weekly, in-person communication between scholars and mentors is a must.

IV. A Look at Causal EffectsThe correlation analysis shed light on the possible linkage between the frequency

of communication, method of communication, and level of connectedness between mentors and scholars. The subsequent regression analysis then predicted appropriate frequencies and methods to best maximize the level of connectedness. Though these statistics provide insights into the current status of our mentor-scholar relations and how they could be improved, they are too limited to suggest any causal relationships.

However, the fourth survey question, “If necessary, how can we help you get to know your mentor better?” requires scholars to reveal what challenges they faced during their mentorship experience. If the method of communication were the problem for some scholars, it would be revealed in their answer to this question. If the frequency of

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communication were the problem for some scholars, it too would be revealed in their answer to this question. Because the question is open-ended and requires scholars to share their direct experiences, the answers to these questions provide insight into what actually causes impactful relationships to form. As such, I have listed some of the actual responses to that question.

On the method of communication, many scholars revealed that would prefer in-person meetings, suggesting that e-mentoring was not the most useful model for personal interactions:

“I would have liked the opportunity to meet my mentor in person…”“I think it would be better if some mentors were around the area of the student.”“May I suggest a get-together of Phoenix Scholars mentors and mentees after

senior year is over…”“The best way to better know my mentor would probably be to hold a get together

where both the mentor and the mentee must attend.”“The most effective way I believe is meeting…in person…”“However, it would be nice if a general Phoenix Scholars meet-up was set

up…”On the frequency of communications, many scholars also revealed that having

more frequent, mandatory communication would be beneficial for them, suggesting that quarterly and even monthly communication is inadequate:

“Require a mandatory Skype call…”“By scheduling mandatory check-ins at least once a week, mentees could get to

know their mentor better!”“[Make] it mandatory to text, call or have some type of communication once a

month at the least.”“More communication…can always be a bit helpful.”“Having a schedule that was determined would make it easier…”The qualitative answers to the fourth question of the survey validate the statistics

after all. Though the correlation analysis revealed a weak, positive correlation between method of communication and level of connectedness, the number of scholars saying they want more in-person meetings affirms that the correlation is even stronger than initially predicted. The correlation analysis also predicted a moderate, positive correlation between the frequency of communication and level of connectedness. Again, the scholars’ answers reveal that there is a strong correlation between those two variables. Further, the responses to the fourth question confirm the solutions provided by the regression analysis, i.e., scholars would like to have more in-person meetings with their mentors, and they would also like to be in more frequent communication with their mentors.

Ultimately, these results demonstrate that my hypothesis was only partially valid. Though the method of communication is a vital component in scholars getting to know their mentors better, the frequency of communication is equally important in relationship building.

Moving Forward Given the results from the survey, establishing a set of policy recommendations

for The Phoenix Scholars is a logical next step. The goal of such recommendations

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should be to make frequent, in-person communication between scholars and mentors as feasible as possible. To accomplish this, recruiting students closer to Stanford’s campus, providing awards and scholarships to incentivize mentor and scholar participation in TPS, and reducing the yearly cohort size are steps in the right direction.

Recruiting Closer to StanfordAs mentioned earlier in the paper, Stanford is based in an area of affluence. As

such, the program’s Outreach Committee has historically traveled long distances to present at high schools in some of California’s most underserved communities. The assumption was that the organization’s target demographic was most easily accessible in such communities. However, that assumption is not necessarily true. The California State Association of Counties publishes “California Low Performing Schools,” a list of underachieving schools by county.13 Interestingly, 27 schools on that list are within a one-hour drive of Stanford University, with one of those schools being only eight minutes away from Stanford’s campus. Though “low performing” does not mean that those schools necessarily serve low income, first generation, and minority youth, those social indicators are typically present in underperforming settings. Thus, refocusing the outreach efforts of the organization could result in recruiting students closer to Stanford, making in-person meetings between mentors and scholars more feasible. There is also the possibility that The Phoenix Scholars could host events on campus to help facilitate those in-person meetings.

Providing Awards and ScholarshipsIn addition to recruiting scholars from nearby schools, The Phoenix Scholars

could also provide awards and scholarships in the form of textbook vouchers, application waivers, and cash gifts to those scholars who are actively engaged in the program. Ways to gauge engagement include having their mentors submit a letter of recommendation and tallying the number of monthly tasks scholars have completed. In any case, providing awards can incentivize scholars to remain involved and easily accessible throughout the course of the 12-month program.

Reducing Cohort SizeIn the same way scholars need to be actively engaged in the program, the TPS

executive team needs to be actively engaged with its scholars. More specifically, the executive team should be in a position to effectively monitor relationships between mentors and scholars via surveys, interviews, or group discussions. By being more proactive in helping nurture these relationships, TPS can identify problem mentors and scholars before something as radical as the “intervention” held in October happens. Perhaps by reducing the yearly cohort size by a third or even half, the organization can fine tune the ways in which it tracks the growth and progress of mentor-scholar pairs and afterwards focus on scaling.

Conclusion This study aimed to discover whether or not the e-mentoring model was an

effective one for The Phoenix Scholars. Survey results showed that scholars were more impacted by the lack of in-person meetings than by e-mentoring itself. Further, scholars

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desired required, more frequent communication. In “Moving Forward,” a set of policy recommendations outlined how The Phoenix Scholars can effectively address these experiences. Ultimately, this study hopes to encourage The Phoenix Scholars and any other organization seeking to implement technology to test the implications of said technology before assuming it is beneficial by default.

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11. Bierema, L. L. & Merriam, S. B. E-mentoring : Using Computer Mediated Communication to Enhance the Mentoring Process. Innov. High. Educ. 26, 211–227 (2002).

12. Rumsey, D. J. How to Interpret a Correlation Coefficient ‘r’. Statistics For Dummies, 2nd Edition (2011). at <http://www.dummies.com/how-to/content/how-to-interpret-a-correlation-coefficient-r.html>

13. California State Association of Counties. California Low Performing Schools. at <http://www.csac.ca.gov/pubs/forms/grnt_frm/LowPerformingSchool.pdf>

Appendix The original data from the online survey can be found by visiting https://docs.google.com/a/stanford.edu/spreadsheets/d/1CCTZRa2cRWz42t9ehrKPKhVzw_1R7OVaYGeNDg5TFC8/edit?usp=sharing.