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STEM Near Peer Mentoring for Secondary School Students: a Case Study of University Mentors’ Experiences with Online Mentoring


The steady decline in secondary students’ achievement and interest in science and mathematics is an area of concern for governments, industry, and the education sector. Increasing student engagement in Science, Technology, Engineering, and Mathematics (STEM) disciplines is a top priority if countries are to meet demands for STEM based expertise in the current and future workforce. Amongst strategies to address such concerns, peer mentoring programs have gained increased popularity. Although previous research shows that mentoring can be a successful strategy, the processes that underpin positive outcomes for participants remain largely underexplored. This paper responds to calls for research on mentoring processes linked to effectiveness in STEM peer mentoring programs. We explore the development of mentoring relationships in an online near peer mentoring program between university students and regional secondary school students. We analysed qualitative and quantitative data on mentors’ perceptions of relationship quality and mentee engagement, and their use of mentoring strategies, over a 9-week period. Implications for online mentoring are discussed, including a model to facilitate university-to-school mentoring to increase students’ engagement in STEM disciplines.


Online, near peer mentoring holds promise to address the Organization for Economic Cooperation and Development’s (OECD 2012) education priority to improve students’ engagement and achievement in STEM (Science, Technology, Engineering, and Mathematics). In near peer mentoring, students participating as mentors are one or more years senior to their mentees (Akinla et al. 2018). Newhouse (2017) recognizes the need to create STEM graduates for the current and future workforce. Yet, Osborne and Dillon (2008) and Kennedy et al. (2014) report enduring declines in secondary and university STEM participation. To stop and reverse declines in participation and meet the OECD’s objectives, Tytler’s (2007) work identifies the necessity for interventions aimed at children under the age of 14 years; then, children have already established their scientific identities.

A suite of interventions offers opportunities to secure STEM engagement and to raise achievement in young students. Rhodes et al. (2011) note the positive influence of mentoring on all students and, in particular, those from low socioeconomic backgrounds who often lack scientific role-models. For those students fortunate to access mentoring opportunities, mentoring scientists engage with students and teachers to act as role models (Sadler et al. 2010; Scogin and Stuessy 2015). To broaden the mentoring pool, recruitment of university students has received endorsement from various quarters in Australia (e.g. Commonwealth of Australia 2015). The literature on peer mentoring recognizes the positive impact of scientific role models on high school students. Benefits of mentoring include improved academic performance (Sharpe et al. 2018; Pluth et al. 2015), enriched attitudes to science (Simpkins et al. 2006; Tenenbaum et al. 2014), and self-confidence (Stoeger et al. 2013). Traditionally, these benefits occur when mentor and mentee can meet face to face. To universalise the benefits attributed to near peer mentoring designed to enhance STEM retention, recruitment strategies require the establishment of connections between university and school students located in different locales. For example, Garrison’s (2011) solution recognizes the utility of online communication tools to overcome spatial barriers to extend mentoring benefits to students in regional and rural areas.

Putting aside for the moment, the medium of communication, peer learning researchers (e.g. Riese et al. 2012) note that studies often focus on programmatic outcomes rather than the processes mentors used to achieve the outcomes. For example, DuBois et al. (2002) emphasized the importance of identifying specific process-level factors that can influence mentoring outcomes. Following this recommendation, Goldner and Mayseless (2009) proposed the mentor-mentee relationship represented a causal, and yet under-researched mentoring process. To fill in this gap in mentoring relationships research, Rhodes et al. (2005) call for intensive monitoring of relationships, while Schunk and Mullen (2013) suggest the use of research methodologies that explore how relationships evolve over time. Turning to the online mentoring environment, the added challenges of communicating via multi-media and web-based conferencing, the importance of online mentoring strategies to establish relationships remains underexplored. In particular, Leidenfrost et al. (2011) argue for the need to further investigate what type of online mentoring activities participants engage in. Here we focus on mentors’ experiences in a university-to-school synchronous online mentoring program aimed at increasing secondary school students’ engagement in STEM. We measured the frequency and importance of mentoring strategies to enhance mentor-mentee relationships, as well as mentors’ perceptions of the quality and development of the relationship with their mentees. To provide a context for this study, we first review mentoring programs in STEM, then explore notions of quality in mentoring relationships, and within online peer mentoring programs in particular.

Mentoring Programs and STEM

Mentoring initiatives aim to empower students’ personal development and compensate for the lack of role-models (Rhodes et al. 2011), in particular, for students from low socioeconomic backgrounds. These programs can vary in delivery format (face-to-face, online, or a combination of both formats), number of participants (one-on-one, one-to-many, many-to-many), scope and purpose, and degree of similarity between mentor and mentee - amongst other factors (Topping and Ehly 2001). Despite the variety of roles that mentors can accomplish, the literature recognizes four distinct functions: psychosocial or emotional support, role model, assistance in goal setting and career paths, and subject-specific expertise (Nora and Crisp 2007). These functions vary across different mentoring programs, with some combining several functions to provide both academic and socio-emotional support to mentees.

Mentoring programs matching students with scientists have become increasingly popular (Scogin and Stuessy 2015). Researchers often report positive outcomes of mentoring programs; in particular, programs directed to STEM high school students can lead to improved student perceptions of science as more relevant and science careers as more interesting (Scogin and Stuessy 2015). Encouraging students to enrol in STEM subjects, and to consider career choices in these disciplines, is typically a main goal of these programs. Amongst STEM mentoring strategies, peer mentoring programs employ current STEM students as mentors to increase other students’ engagement and retention in STEM disciplines. Peer mentors can improve students’ academic performance in STEM disciplines, content knowledge, attitudes about science and mathematics subjects, and self-confidence (Bowling et al. 2015; Cutucache et al. 2016). Although STEM peer mentoring can be implemented in any educational level, targeting middle school students is of particular importance. Positive STEM experiences in middle years can have long-lasting effects on students’ scientific aspirations, as research shows that students have made identity related decisions about their future by the age of fourteen (Tytler 2007). Peer mentors can role-model positive attitudes towards science, helping students consider a career in STEM disciplines. Moreover, peer mentors also benefit from their participation, gaining effective science communication skills and increased confidence (Nelson et al. 2017), and sharing their enthusiasm about science with school students (Pluth et al. 2015). For example, in a qualitative study of a mentoring program between STEM undergraduates and high school students, Tenenbaum et al. (2014) found that mentors obtained personal, educational, and professional benefits, whilst mentees increased their interest and engagement in STEM related disciplines. Similarly, Sharpe et al. (2018) found that senior higher education students’ attitudes towards science improved after being mentored by university students over the course of one academic year.

Quality Indicators of Mentoring Relationships: Social and Cognitive Congruence

Youth mentoring researchers have characterized the quality of mentoring relationships by the frequency of contact mentor-mentee, emotional closeness, and longevity of the relationship (DuBois et al. 2002). Goldner and Mayseless (2009), in their study of a youth mentoring program, demonstrated a clear association between the quality of the relationship and improvements in mentees’ academic and social functioning. Dubois and Neville (1997) also found that relationship closeness resulted in increased benefits in a community-based mentoring program between undergraduate students and teenagers. Rhodes et al. (2006) further note that high quality mentoring relationships feature mentor-mentee closeness, authenticity, empathy and empowerment. Research on peer mentoring relationships in educational contexts shows that effective mentors are able to respond to mentees’ academic and psychosocial requests (Ward et al. 2014). In order to achieve such level of response, mentors make use of the cognitive and social similarities shared with mentees. Subsequently, we delineate these factors into social and cognitive congruence respectively (Ten Cate and Durning 2007).

Social congruence refers to perceived social similarities between mentor and mentee (e.g. shared educational experiences), and allows for the development of empathy, trust, and self-disclosure (Dioso-Henson 2012). Ten Cate and Durning (2007) situate social congruence within the affective and motivational level of learning, and argue that peer mentors are better placed than academic staff to understand students’ motivations due to their status as fellow students. Social congruence is strengthened when mentors share information about their learning experiences and disclose their own learning challenges, past or present (DuBois et al. 2011). In turn, when mentees feel understood by mentors, they are more likely to self-disclose learning gaps, enabling subsequent support from mentors. Complementary to social congruence, cognitive congruence relates to mentors’ ability to understand mentees’ cognitive and learning challenges, and anticipate learning difficulties. Peer mentors are able to understand and operate at mentees’ level of development because they have recently transitioned through that developmental level (Lockspeiser et al. 2008). An emergent property of cognitive congruence occurs when mentors can identify mentees’ learning gaps and employ scaffolding and language suitable to mentees’ current cognitive development (Ten Cate et al. 2012). When paired with social congruence, mentees perceive their learning difficulties are not only understood but also addressed by mentors through explanations suited to their learning needs.

Successful mentoring relationships are based on both social and cognitive congruence (Garcia-Melgar et al. 2015), however, social congruence is usually established earlier in the relationship than cognitive congruence. According to Rhodes et al. (2005), a strong emotional connection needs to be established before participants can proceed to achieve the objectives of the program or academically support the student. Mentors who manage to establish and maintain quality relationships with their mentees tend to include transformation goals after having worked on relationship quality goals (e.g. by learning about mentees’ interests and building trust), and are able to build activities upon mentees’ interests. Thus, social congruence needs to be firmly established before mentors can make use of their cognitive congruence to provide academic support.

Not all mentoring relationships, however, are equally effective. Mentors’ preconceived and firmly held role expectations can positively or detrimentally influence the relationship (Capstick 2004). For example, mentors who do not feel efficacious early in the relationship (because they do not perceive their mentor-mentee relationship to be strong, positive, or effective) remain unlikely to persist in the partnership (Karcher et al. 2005). Karcher and colleagues also argue that reasons for volunteering are central to program success and mentor persistence. In their study of adolescent - elementary age mentoring relationships, the authors found that mentors’ perceptions of relationship quality were mainly a function of mentees’ openness to seeking support from mentors and mentors’ initial feelings of self-efficacy. They concluded that it is critically important to continually assess and monitor mentors’ motivation and promote their self-efficacy, as the attitudes mentors initially hold about mentoring can potentially threaten the success of the mentoring program.

Online Mentoring Programs

Online mentoring can take place in synchronous and asynchronous formats. Interactions via text-based media characterize asynchronous programs, while synchronous programs rely on video and audio conferencing tools (Schwartzman 2013). Research on online mentoring demonstrates that the online communication environment provides diverse advantages to participants, compared to face-to-face mentoring programs. Scogin and Stuessy (2015) argue that online mentoring may equalize the status of all participants as interactions are less affected by demographic characteristics. Consequently, mentors and mentees can base their interactions on shared common interests and goals rather than perceived similarities or differences based on demographic factors. Ensher et al. (2003) further contend that online participants have the opportunity to spend more time working on self-presentations and developing positive relationship experiences, compared to face-to-face participants. Furthermore, some mentoring participants may find online technologies less intimidating than face-to-face interactions (Scogin 2016).

Despite the increasing importance of online mentoring, reports on specific mentoring dynamics remain relatively few. Ensher et al. (2003) argue that online mentors accomplish the same functions as traditional mentors. Therefore, effective online mentors can provide educational and psychosocial support, as well as role-modelling of positive attitudes and skills. Although online relationships may take more time to develop (as participants may exchange, on average, less information than in face-to-face mentoring) online participants can develop strong mentoring relationships (Chidambaram 1997). In their study of an online mentoring program that matched scientists and secondary school students, Scogin and Stuessy (2015) found that mentors provided motivational support that increased mentees’ engagement in science despite only using text-based communications. In a study on the effectiveness of online Supplemental Instruction sessions (peer-led support sessions that target students in high-risk of failure university subjects), Hizer et al. (2017) found equivalent academic outcomes for students participating in online and face-to-face sessions, suggesting that the online environment did not hinder the effectiveness of the program.

A number of challenges exist for online mentoring programs, however, and these mainly relate to issues around sustaining engagement via online communication platforms - in particular, for programs where mentors and mentees only interact online. Participants’ limited digital literacy skills can affect online interactions, as well as changes in participants’ motivation to engage in mentoring, and frequency of communication between participants (Shpigelman and Gill 2013). In a study of online mentoring between scientists and secondary students, Scogin and Stuessy (2015) found that, although mentors provided motivational support, there were differences in the amount and type of support they provided. The authors attributed these variations to challenges inherent to communicating in an online environment and suggested further training for online mentors. Ebby et al. (2010) also found that technology malfunctions, increased miscommunication opportunities, and participants’ written communication skills can severely limit the development of mentoring relationships in online platforms. Shpigelman and Gill (2013) studied unsuccessful online mentoring relationships to conclude that these were characterized by mentors’ use of a more formal and distant communication style with mentees. Unsuccessful interactions also featured high levels of uncertainty about how best to use an online format, which led the authors to suggest that online mentoring should be built around specific discussion topics. On the contrary, successful mentors employed a conversational style, asked direct questions to their mentees, and provided them with further learning resources.

Finally, asynchronous and synchronous communication tools pose different challenges to online mentoring programs. Asynchronous tools are usually easier to use and technologically reliable (Gregg et al. 2016). Asynchronous tools, such as discussion forums or emails, can provide participants with access to past conversations and can be monitored by program administrators and teachers involved in the mentoring program (Sherman and Camilli 2014). The lack of real time interaction, however, can result in decreased engagement, especially if mentors and mentees do not keep frequent and timely contact (Scogin 2016). On the other hand, synchronous communication tools - such as web-conferencing - aim to address asynchronous tools shortcomings, as participants can employ valuable verbal (e.g. words and tone of voice) and non-verbal information in their mentoring interactions. As a result, these tools can increase participants’ sense of connection and reduce psychological distance between participants (Schwartzman 2013). Studies on the effectiveness of synchronous communication tools, however, have found mixed results. Beaumont et al. (2012) studied online peer assisted programs in higher education to find low student uptake to be one of the main challenges. Participants’ sense of connection with mentors was also low, despite communications taking place through audio and video conferencing. Hizer et al. (2017) investigated the effectiveness of peer-led review sessions at university, employing synchronous and asynchronous communication tools, and found that students preferred chat-based interactions over real time mentoring sessions. All in all, research on online mentoring programs remains an area to be further developed, with online mentoring researchers calling for further studies on the effectiveness of synchronous communication tools and their potential to overcome challenges present in text-based mentoring programs.

Current Study: Research Aims

The growing ubiquity of online mentoring programs, and their potential to increase student engagement and interest in STEM disciplines, warrant further research on processes and mentor-mentee dynamics underpinning effective mentoring between students. The main goal of this study is to investigate mentors’ experiences within an online near peer mentoring program between STEM university undergraduates and middle years secondary school students, and to identify strategies to maximize the potential of STEM online mentoring. We follow Leidenfrost et al. (2011), and Zaniewski and Reinholz (2016) recommendations to monitor the development of mentoring relationships and assess the type of activities mentors engage in. The middle years of schooling provide a unique context to investigate STEM mentoring, as mentors can have a positive impact on the development of students’ attitudes and interest towards STEM disciplines and STEM related careers. In particular, this study seeks to address the following questions:

  • What strategies do mentors employ to engage mentees and develop effective online mentoring relationships?

  • What changes to the quality of mentoring relationships do mentors report over the duration of the program?

  • What challenges do mentors encounter in online mentoring environments, and what strategies can be put in place to support online mentors in STEM programs?

This study employs mixed-methods to explore how mentors develop and engage in online mentoring relationships, what strategies they use to engage mentees, and how they respond to challenges inherent to online mentoring environments. We employ a convergent parallel design (Creswell and Plano Clark 2017) to collect and combine qualitative and quantitative data on mentors’ perceptions of relationship development and weekly post-mentoring reflections, with the objective of illuminating factors contributing to the development of online mentoring relationships. Moreover, the analysis of mentors’ experiences and perceptions of relationship development reveal valuable insights into the type of support that mentors may need to successfully engage in online near peer mentoring relationships.

Methodology and Data Analysis

Research Setting and Participants

Participants were mentors participating in In2science eMentoring, an online mentoring program that places university undergraduate STEM students as mentors for year 9–10 students (14–15 years old) in regional and rural areas in the state of Victoria, Australia. The purpose of the program is to increase achievement in STEM disciplines, inspire passion for science and mathematics, and increase engagement in further secondary and university STEM education. The program is supported by the Victorian Department of Education and Training, and recruits undergraduate students from four partner universities in the metropolitan area of Melbourne who volunteer to serve as online mentors. The eMentoring program aims to target Victorian schools in regional or remote settings with a higher density of students from low socio-economic backgrounds. The program seeks to address the added disadvantage of distance through the provision of online mentoring opportunities with STEM undergraduates, and follows a developmental mentoring approach (Feldhaus and Bentrem 2015) focused on tailored support and advice to mentees.

Six schools were recruited for the pilot of the program, based on their Index of Community Socio-Educational Advantage (ICSEA), Student Family Occupation (SFO), and regional or remote setting. Once schools agreed to participate, the Science and Mathematics teachers distributed information about the program to their year 9 and 10 students. Teachers discussed program requirements and expectations (i.e. willingness to engage in weekly online mentoring sessions) with interested students, and submitted a list of student/mentees alongside their STEM interests, availability, and preference for one-on-one or group mentoring. The program offered group mentoring (one-to-many) to students working on group projects, with a maximum of three mentees by mentor group (based on a pre-pilot of the program that showed that mentors struggled to communicate online with more than three students). Thirty-nine student/mentees participated in the pilot. Mentees and mentors were matched based on mentees’ STEM interests and mentors’ discipline areas of study: six mentors had a group of three students, seven mentors had two students, and five mentors had one student. Mentoring sessions took place on a weekly basis for 9 weeks, commencing at the beginning of the second university semester. Participants communicated via a customized online platform that allowed for video conferencing, screen sharing of resources and text-based chat. Session content was agreed upon between participants, including discussions around science career aspirations, science and maths topics covered in mentees’ classes, life at university, and effective study strategies. Mentoring sessions typically lasted between 40 and 60 min and took place during school time for mentees. Mentors and mentees used a quiet room at their university or school, respectively, and accessed the online meeting room using laptops or individual IPADs.

Eighteen mentors participated in the pilot of the eMentoring program; the majority of mentors (12) were females. Mentors were studying their second or third year of a STEM undergraduate course: the majority of mentors were studying Biochemistry and Chemistry related degrees (n = 7), followed by Engineering students (n = 5) and Biomedical Sciences (n = 3). The remainder of mentors were studying Physics, Agricultural, and Animal Science. All mentors participated in a 3-h online training session prior to commencing mentoring sessions. The session covered aspects of effective mentoring relationships and mentoring theory, including social and cognitive congruence. Mentors also received training on the online communication tools and resources they could use (screen and video sharing, online activities and quizzes) and strategies to engage students in online environments. Mentors were instructed to discuss science topics (related to topics covered in mentees’ classes) as well as students’ aspirations and STEM careers and educational pathways. Mentors planned their sessions with classroom teachers’ support, who provided information about the content that mentees were covering during the 9 -week period, and had access to an online repository of activities and multimedia content organized by areas of the Victorian school curriculum.

Data Collection

Data collection took place during an extended pilot of the eMentoring program. Mentors were invited to participate at the beginning of their 9-week placements and were asked to complete a pre-placement questionnaire on their motivations to become a mentor, confidence in their mentoring skills (5-point Likert scale), expectations about the type of tasks and discussions they would engage in with their mentees, and foreseen challenges. After each weekly mentoring session, mentors completed a post-session questionnaire that asked them to provide an account of what was discussed during the session, how it was organized, student engagement and dynamics, and their notes for the next session. To preserve anonymity of responses, a unique personal code was employed to match participants’ responses across mentoring weeks. The questionnaire also included a list of strategies that mentors could use to enhance the quality of mentoring relationships as described in the peer mentoring literature, as well as areas of discussion/conversation. These were adapted from the literature on characteristics of effective mentors (e.g. building upon students’ interests), and included factors related to social and cognitive congruence aspects (e.g. use of practical examples, self-disclosure). Mentors rated the importance of each strategy after each session (5-point Likert scale) and identified alternative strategies where applicable. The list of strategies is included in section 1 of the supplementary file and encompasses types of topics of discussion/engagement (e.g. science topics, study skills, mentors’ university experiences, STEM careers/aspirations), and strategies designed to elicit responses to determine the relative importance of social and cognitive congruence. Finally, mentors reported on the quality of the mentoring relationship using a 5-point Likert scale of agreement/disagreement. Items were adapted from Karcher et al. (2005) mentoring relationship quality scale, and included strength of mentor-mentee bond, satisfaction with the development of the relationship, mentees’ willingness to participate in mentoring, mentees benefiting from mentoring (as perceived by mentors), and ease of communication.

Data Analysis

Data collected on strategy frequencies and relationship indicators were exported into SPSS 26 to derive descriptive statistics. Quantitative data analysis was guided by the assessment of inflexion points in mentors’ reported frequencies of strategies and quality of relationship indicators. Qualitative data on mentors’ post-session reflections was exported into NVivo 12 to thematically code responses, which were analysed through several coding rounds using a coding frame (Schreier 2012). Each post-mentoring reflection was coded along three dimensions: student engagement (as perceived/reported by mentor), mentors’ use of strategies to achieve or maintain social and cognitive congruence, and session content alongside activities employed by mentors.

We first coded evidence of student engagement – or otherwise- reported by mentors. For instance, statements such as ‘students were curious to know about how complex numbers are used in electronic coding and how it works’ were coded as positive student engagement. We then used the frequency of student engagement to classify each session as successful, partially successful, or unsuccessful in terms of overall mentee engagement, and calculated the percentage of each type of session across mentoring weeks. Sessions were coded as ‘successful/positive engagement’ when mentors reported consistent engagement in activities/content, for all mentees if in a group, throughout the session. If mentee engagement was inconsistent, or not all mentees were equally engaged, the session was coded as ‘partially successful/partial engagement’. Where mentors reported specific factors affecting engagement, these were used to label the session (e.g. technology issues). Finally, if mentors noted consistent indicators of low engagement throughout the session by most participants (e.g. not completing activities, no interaction with mentor) the session was coded as ‘low engagement/unsuccessful’.

In a second coding round, we thematically coded mentors’ use of strategies to achieve or maintain social and/or cognitive congruence with mentees. To do so, coders assessed strategies against descriptions of each type of congruence. Qualitative data on mentors’ reported use of congruence strategies complements quantitative data on frequency/use of mentoring strategies, as it provides detailed descriptions of how mentors employed these strategies – and for what purposes - throughout the mentoring program. In the final round of coding, researchers focused on the content of the sessions and activities/interactions employed by mentors, coding them by session topic (science and mathematics topics, STEM careers and pathways, life at university) and session activity (conversation, presentation, activities). We then calculated frequency of session topics and activities across the total of mentoring sessions. One member of the research team conducted each coding round, with a second researcher coding 25% of randomly selected mentoring reflections to determine inter-coder reliability. Cohen’s Kappa was calculated for each variable, ranging from .80 to .90. Coders discussed and reconciled coding differences until a Kappa coefficient of .90 was reached for each variable. The final nodal structure of mentoring strategies and session content was reviewed by In2science staff members. To discern interactions between topics – and between engagement, strategies, and activities – we conducted matrix-coding queries using NVivo.


Firstly, we present data on mentors’ pre-mentoring expectations and self-confidence in their mentoring skills. We then summarize mentors’ perceptions of relationship quality, as well as frequency and relevance of specific mentoring strategies over the mentoring placement. Lastly, we present qualitative data results on mentors’ session summaries, including mentors’ perceptions of mentee engagement.

Mentors’ Pre-mentoring Expectations and Self-Confidence

All mentors ascribed their participation in the program to altruistic motivations to inspire passion for science, with the majority expressing their interest in helping students in regional areas (85.7%). Opportunities to apply their knowledge of science (78.6%) and career development (64.3%) were also important to mentors. Gaining work experience in schools held limited interest (35.7% of mentors). All participants anticipated they would explain science and maths topics and discuss STEM careers with mentees. Helping students with their homework (92%) and talking about life at university (85%) were also common expectations. Only half of the mentors expected to talk about their university studies with mentees.

Mentors expressed confidence in their mentoring skills, especially their ability to maintain mentoring role boundaries (M = 4.79, SD = .426) and to inspire interest in science in their mentees (M = 4.43, SD = .646). Overall, mentors felt highly prepared to participate in the program (M = 4.14, SD = .770). A common concern amongst mentors related to the online mentoring environment. Their reservations were born from questions about their ability to interact and engage with students. Maintaining mentees’ interest, encouraging their participation, and establishing rapport without physically meeting them were noted to represent potential constraints. Communicating science at the students’ level was also a potentially challenging aspect of the program, according to mentors.

Mentors’ Perceived Relationship Quality Indicators

Table 1 shows averages for each relationship indicator as reported by mentors. Over the 9 weeks of online mentoring, mentors felt that the relationship grew stronger, and the mentor-mentee bond was strong. Mentors’ perceptions of benefits for mentees were the highest amongst all relationship indicators, whilst negative indicators (feeling distant and frustrated) remained low. Mentors’ perceptions of communication ease and feelings of frustrations showed high standard deviation coefficients, reflecting variability in their mentoring experiences.

Table 1 Average scores for relationship quality indicators over 9 weeks of mentoring

All relationship indicators varied throughout the duration of the mentoring program. The following figure represents variations in how mentors experienced and perceived relationships with their mentees over the 9 weeks of mentoring (Fig. 1).

Fig. 1

Mentors’ reported relationship quality mean scores over 9-week mentoring period

Although all indicators improved as mentoring placements progressed, there were slight decreases in relationship quality perceptions in specific weeks. Mentors’ perceptions of distance increased in weeks six and eight. Likewise, feelings of frustration about mentoring increased in week eight, but remained below neutral levels for all other weeks. Perceived ease of communication with mentees peaked in week five and declined in subsequent weeks. It reached its lowest in week eight, but experienced an increase in the last week of mentoring. Mentors reported that mentees’ willingness to learn remained high throughout the program with a marked increase in the last mentoring session. Mentors’ perceptions of mentee benefits derived from participation mirrored this pattern. Finally, the strength of the mentor-mentee bond remained neutral except in weeks seven and nine. Mentors’ perceptions of the relationship as getting stronger remained the highest in the central weeks of the program and the final week, reaching lower levels in weeks two and six.

Strategies Employed by Mentors

Table 2 reports the average importance placed in mentoring strategies throughout the program. Mentors’ responses were highly variable, suggesting that individual mentors held different views on the importance of each strategy.

Table 2 Average importance of mentoring strategies and actions

Overall, mentors reported asking for students’ input to plan and conduct their sessions (i.e. asking mentees about what they would like to discuss and do during mentoring sessions), which featured mentors explaining science and mathematics topics, and using practical examples (i.e. applications of science and mathematics). Study strategies rarely featured in terms of importance, nor did mentors report they regularly disclosed their own learning challenges as a frequent strategy. Again, mentors’ assessment of the importance of these strategies varied. High variability could also be due to the sample size of this study.

Mentors’ Perceptions of Mentees’ Engagement: Changes over Time

Most mentors reported mentees’ positive engagement and interest in mentoring sessions, with mentees reportedly expressing interest in session content, participating in activities set up by mentors, asking questions, and engaging with mentors in science discussions. For example, participant 15 described how “students were pretty engaged, and we started talking about astronomy. They both seemed quite engaged and were chatting to me…they were also doing a work sheet and writing a lot as well”. 41.2% of mentors’ comments noted positive mentee engagement, with 22.2% referring to partial engagement and 28.5% of comments accounting for low engagement in mentoring sessions. Remaining comments referred to mentors’ uncertainty regarding mentees’ engagement levels.

When sessions were coded by overall mentee engagement (positive, partial, low, or affected by specific factors), most sessions were characterized by positive engagement (46.87%), with 26.56% of sessions representing partial mentee engagement. For instance, participant 2 reported “the students are seemingly participating, but they sometimes talk to each other rather than the mentor”. Successful mentoring sessions featured drawing connections between science topics and engagement in further discussions with mentees: “We got into good discussions again, such as the existence of aliens and what discoveries can still be made. I linked in the mathematical aspects of space sciences because we hadn’t looked at math much this semester” (Participant 9). Mentors also made explicit connections between science topics and mentees’ interests and aspirations: “I also linked it to animals, as one of the girls wants to be a vet, by detailing how bacteria can affect animals differently” (Participant 13).

Mentors only reported 6.25% of sessions as featuring low or negative mentee engagement. In 12.5% of overall sessions, mentors noted that technological issues affected engagement (e.g. slow internet connection, audio or video not working properly); 7.8% of sessions were impacted on by other factors, including disruptions to sessions or scheduling conflicts with class time. Participant 12 describes external factors disrupting their mentoring session: “the class in the next room was very curious to see what was going on, which was distracting but fun”, whilst participant 2 refers to clashes with other classroom activities: “session was limited to 20 minutes as students needed the rest of the session time to prepare for their project”.

Although most weeks featured positive engagement, partial engagement occurred in weeks one, three, five, and six (Fig. 2). The last week of the mentoring placement was characterized by positive engagement/successful sessions, consistent with mentors’ assessments of relationship quality indicators improving towards the end of the 9-week period. Low/negative engagement remained low throughout the mentoring placement, with most unsuccessful mentor-mentee interactions occurring in week eight. Technology issues affecting mentees’ engagement were most relevant in week six, whilst external factors (mentoring sessions clashing with other student activities, external disruptions, shorter sessions due to other student activities) had a negative impact on the overall success of sessions in weeks six and eight. In addition to technology issues and external factors, mentors working with groups of students noted that inconsistencies in some students’ attendance were also a disruptive factor (e.g. mentors needing to repeat content for students who had not attended a previous session).

Fig. 2

Mentees’ engagement in sessions as reported by mentors – session percentage by weeks

Social and Cognitive Congruence

Mentors’ strategies to build social congruence with mentees focused on finding common scientific interests, getting to know students’ interests and aspirations, and disclosing their background and learning experiences. This finding aligns with mentors’ reported use of self-disclosure of learning experiences and interests/aspirations. An example of self-disclosure is given by Participant 8: “I told them that when I was younger I wanted to be a chef or a pilot, with the student who wants to be a chef surprised”. Mentors appreciated the value of building rapport to subsequently providing meaningful guidance on education themes (e.g. subject choices, changing career aspirations), exemplifying how they used mentees’ interests to tailor session content. For example, Participant 1 reports that, upon finding his mentee was interested in medicine or dentistry: “I committed to researching his tertiary options to provide a framework for his aspirations, and asked that he take note of any areas of interest of problems in class to discuss throughout our sessions”. Since mentors and mentees did not share similar social/educational roles, mentors deliberately focused on actions to establish social congruence to find common ground in order to better understand their mentees: “Next week I will be talking about what I do at uni as they were interested to learn more about biotechnology” (Participant 13).

Mentors reported more on the products of cognitive congruence (e.g. mentees found presentation engaging) than strategies to build cognitive congruence. When they mentioned strategies, it was usually as part of the challenges they had experienced: “I had completely forgotten the level of science I was dealing with when I was their age… how to present information in a way that keeps attention without being overwhelming” (Participant 10), or strategies to improve future sessions (e.g. finding suitable topics, making presentations more simple, maintaining engagement and interest): “I used my experience from the last presentation to make this one a lot more interesting and interactive for the girls, as I felt they found it boring last time” (Participant 15).

Mentors mostly reported challenges in maintaining cognitive congruence; few mentors expressed issues with the social aspects of the mentoring relationship. Although mentors reported using practical examples during their sessions, their reflections revealed challenges and difficulties in finding suitable topics for their mentees as well as pitching the information at the right level. As Participant 4 reported: “The challenges would be finding suitable topics relevant to their knowledge and making the work as simple and in year 9 standard”. Participant 11 reflected on the challenges associated with introducing more advanced topics in their mentoring discussions: “I wasn’t able to explain them technically how it works as it is above their syllabus, but I think I did a good job relating complex numbers and their use in circuits”. Keeping students engaged throughout the sessions also remained a challenge: “I did find the presentation a little challenging. I feel like I had the level of information right, but it was a little hard to hold their interest” (Participant 9). Most of the reported challenges occurred in week five; finding suitable topics was first mentioned as a difficulty in week three, whilst issues related to presenting information at the appropriate level became relevant in week six.

Content of Mentoring Sessions

Mentor-mentee groups engaged in three categories of interactions: discussions, mentor presentations, and activities (Table 3). Discussions represented 65% of reported interactions, with educational themes (e.g. subject choices, moving to university) being the most frequent topic of discussion, followed by science topics. For example, participant 13 noted: “we discussed ATAR (Australian Tertiary Admission Rank) and the changing perception by uni, as well as the importance of extracurricular and leadership stuff”. Conversely, science discussions ranged broadly, and included biology, chemistry, physics and maths topics – to name a few. General conversations (e.g. catching up after school break, talking about students’ activities after school) represented topics mentors believed important.

Table 3 Session content/activities and mentee engagement

Presentations for mentees represented the second most common interaction (17%), while a close third included diverse activities (16%) such as showing videos, working through worksheets, or engaging in structured activities. The majority of presentations focused on science topics: “I had prepared a short presentation on one of the topics they had come up with last week (yellow fever). I had made it up like lecture notes that we use at university to give them an idea of what a lecture is like” (Participant 5). Amongst activities, online videos followed by activity questions were the most popular type of activity employed by mentors: “This week I was giving the student a head start about the ecosystem, using a video and a range of questions that they can answer from the video” (Participant 16). Most activities occurred within successful/positive engagement sessions. Highly engaging sessions featured diverse and interactive activities, education related discussions and mentors’ presentations on education and science topics. Sessions that were partially successful/ partial engagement featured study skills discussions, teach back activities, and general conversations. Poor/ unsuccessful sessions featured low usage of activities, discussions, and presentations.

Mentors’ strategies to improve subsequent sessions – based on reported challenges - focused on preparing sessions, finding appropriate and engaging content, and providing more interactive content for mentees. Finally, mentors noted that mentees’ unclear expectations about the mentoring program remained a challenge in the first sessions: “I think the students are a bit unsure of what they can ask me, since they were repeating some of the questions they asked in our last session” (Participant 10).


Secondary school students can benefit from technology-mediated near peer mentoring programs, which have the potential to overcome geographical barriers to reach students traditionally underrepresented in mentoring. This study contributes to the online peer mentoring literature through the investigation of the experiences of STEM university students mentoring secondary school students in regional areas. Results suggest that specific aspects of the mentoring relationship need to be taken into account when designing near peer online mentoring programs to increase STEM engagement. We discuss implications for research and implementation of online STEM peer mentoring initiatives, and propose a university-to-school mentoring model that could overcome difficulties reported by mentors in this study.

Participants enacted characteristics of successful mentors as described in previous studies on online mentoring, namely, enhancing mentees’ participation by asking for their input and feedback on mentoring sessions (Scogin and Stuessy 2015). Providing mentees with opportunities to become active participants, and tailoring sessions to mentees’ science interests and aspirations, were ongoing tasks for mentors over the duration of the program. Most mentor-mentee interactions were conversation based, a factor associated with effective mentoring as it promotes mentees’ active participation and serves to strengthen mentor-mentee rapport (Shpigelman and Gill 2013). In addition to mentors’ strategies to increase mentees’ input, and the use of discussion-based interactions, mentors emphasized the importance of preparing and structuring mentoring sessions – in line with previous arguments around the need to provide specific focus and structure to achieve successful mentoring interactions (Shpigelman and Gill 2013). Mentor-mentee interactions exemplified the provision of academic and social support that characterizes successful mentoring (Ward et al. 2014) through two types of interactions respectively: discussions that supported students’ understanding of science topics; and conversations around pathways to further education. Therefore, mentors aimed not only to increase students’ scientific knowledge but also to inspire educational aspirations in mentees aligned with their scientific interests.

Despite positive increases in relationship quality indicators, the mentor-mentee bond remained at neutral levels, suggesting - as Chidambaram (1997) does - that online mentoring relationships may take longer to develop than face-to-face relationships. Variations in mentor-mentee communication ease point to online communication environments as a potential disruptive factor to relationship development (Shpigelman and Gill 2013). In addition, participants in this study reported a number of challenges that affected their ability to establish and sustain mentee engagement. We discuss these factors and implications for online mentoring next.

Developing Online Peer Mentoring Relationships Using Web Conferencing Tools

The analysis of mentors’ weekly reflections revealed that discussions provided the favoured form of interaction (60% of total), compared to online activities and presentations. Despite the variety of online tools available to mentors (e.g. streamed video, simulations, and other online resources), uses of these technologies represented less than 20% of reported interactions. We suggest that mentors may have chosen to rely on their face-to-face communication strengths and skills in verbally explaining and connecting science topics, rather than using media tools to communicate science, due to their limited experience in using online communication tools for mentoring purposes. Previous studies on online mentoring programs have found that students may opt for the simplest tools of communication (Schwartzman 2013). Fear of technology pitfalls, and limited confidence in digital literacy skills, may have affected mentors’ decision to rely on discussion-based interactions. Technological challenges can potentially disrupt mentor-mentee rapport, and require additional investment of time in subsequent sessions to re-establish bond if online communication tools fail. Improving the reliability of online communication tools could enhance mentors’ confidence to employ a variety of online tools in mentoring sessions, and has indeed been identified as a potential challenge in previous online mentoring studies (e.g. Ensher et al. 2003).

Online mentoring researchers have typically focused on programs that employ asynchronous communication tools, such as text-based communications (Gregg et al. 2016). In the present study, participants employed synchronous tools and communicated via video conferencing - allowing for real time collaboration through screen sharing. The online medium would allow for subtle verbal and nonverbal language cues that would enable mentors to discern and re-establish variable states of engagement (Brennan and Lockridge 2006). The use of real time communication, including video and audio, could overcome shortcomings identified in online asynchronous mentoring programs, such as participants’ disengagement or mentor-mentee miscommunications (Hizer et al. 2017). Mentors in this study, however, seemed to use the video-conferencing platform as an extension of face-to-face mentoring, as they favoured the use of discussions over other online tools available to them (e.g. multimedia or activities). We argue, as do others (e.g. Schwartzman 2013), that online mentoring provides a unique learning environment for participants, rather than simply a translation of face-to-face mentoring to online formats. Encouraging mentors to make use of real-time online activities, other than audio and video-based interactions, could enhance learning opportunities for mentees that would be unique to online mentoring formats. In fact, successful sessions in this study featured a combination of discussions, mentor presentations, and activities such as online videos and screen sharing. We also propose that online mentoring programs could benefit from the use, and advantages, of both synchronous and asynchronous communication tools. Providing mentors and mentees with tools to establish real time communication and increased rapport, on one hand, and off-line spaces for reflection and knowledge co-construction, on the other, could enhance mentee participation and engagement in online mentoring.

The analysis of mentors’ perceptions of mentee engagement, and associated challenges, suggests that cognitive aspects of the mentoring relationship proved more challenging to establish and maintain – compared to social aspects. We discuss mentors’ accounts of cognitive and social relationship aspects using mentor-mentee congruence concepts next.

Two Types of Mentor-Mentee Similarity: Social and Cognitive Congruence

While previous studies have emphasized the importance of mentor-mentee similarity (Stoeger et al. 2013), this study differentiated between two types of similarity or congruence: social and cognitive. Despite congruence being described in the literature as a characteristic of effective peer mentors (e.g. Ten Cate et al. 2012), our study suggests that mentors can struggle to sustain congruence over time. In this section, we discuss challenges related to peer congruence in relation to mentee engagement and mentor-mentee interactions.

Social congruence played an important role in the first weeks of mentoring, as mentors employed strategies to get to know their mentees. In line with previous research on the importance of establishing social congruence at the beginning of mentoring programs (Rhodes et al. 2005), mentors focused on understanding mentees’ science interests and aspirations in order to cement the social aspects of the relationship and find engaging content for future sessions. Relationship building, thus, was a priority for mentors in the initial stages of the program, preceding subsequent interactions focused on educational content (Rhodes et al. 2005). Although strategies directed towards establishing social congruence (in particular, getting to know students) varied throughout the duration of the program, these became an ongoing strategy. Social congruence strategies were of particular importance after school holidays (week eight), when mentors needed to re-establish rapport with their mentees.

Interestingly, the use of self-disclosure strategies was mainly restricted to mentors’ science aspirations and current and past learning experiences, rather than self-disclosure of their own learning challenges. Even though researchers have argued that self-disclosure of learning challenges is important to the development of social congruence (DuBois et al. 2011), mentors in this study preferred to establish this type of congruence through the discussion of their science learning experiences and aspirations. Mentors’ decision to avoid disclosure of past and current learning challenges may be explained by their self-confidence in their mentoring skills, as measured before placements. If mentors were highly confident in their ability to mentor, then they may want to appear to mentees as accomplished students. Alternatively, mentors’ past learning experiences may have been different to those of their mentees and, consequently, they struggled to find common learning challenges to share with mentees. Although mentors and mentees were matched based on shared scientific interests, further consideration needs to be paid to other factors that would increase perceived similarity between mentors and mentees (Dubois et al. 2011). For example, mentees could be paired with STEM university students who share similar learning journeys coming from regional and remote areas.

Compared to social congruence, cognitive congruence proved more challenging for mentors to establish and maintain with younger students. Mentors recognized that, on occasions, they struggled to adjust their explanations or activities to mentees’ knowledge level or science interests. In these cases, mentors endeavoured to re-engage students by asking for their input and discussing their science interests. While as little as 5 years may separate the first-year university student from the grade 9 or 10 student, the developmental and cognitive differences may represent a divide, and in some cases we saw challenges amongst mentors’ self-reported engagement with their younger peers.

Whilst past studies have found that peer mentors can employ cognitive congruence, or similarity, with their mentees to provide support and explanations suited to mentees’ cognitive development (e.g. Ten Cate and Durning 2007), mentors and mentees in this study were in different educational levels and institutions. Consequently, they can be best described as ‘near-peers’ rather than peers. The lack of shared current educational experiences may have negatively impacted mentors’ ability to maintain or establish cognitive congruence. In programs where both mentors and mentees are university students, cognitive congruence is more readily available because senior students (as mentors) have recently experienced the same educational challenges as their mentees (Lockspeiser et al. 2008). Near peers from different educational levels, on the other hand, may have to consciously ‘remember’ their secondary school years to understand mentees’ learning difficulties. In order to overcome the cognitive gap that may exist between near-peers, we propose that university-to-school mentoring programs could benefit from the inclusion of senior school students (e.g. year 12). In this study, university students, as near peers, may be too distant in their cognitive development. Such limitation opens opportunities for nearer peers to undertake an intermediate role in university-to-school mentoring programs in a similar fashion to open triads, where graduate students serve as an intermediary between university staff and undergraduate students (Robnett et al. 2018). Senior secondary school students could work collaboratively with university students to mentor middle-school students, as their cognitive development is closer to that of the mentees. The inclusion of intermediate peers warrants further research consideration as to whether mentees would experience increased learning opportunities. Other strategies to bridge the cognitive distance between mentor and mentee could focus on mentor training, which may include a greater focus on questioning techniques in mentors’ initial training or further development opportunities, in order to re-establish congruence with peers.

In addition to considerations about the role of intermediate peers, we argue that peer mentoring programs should take into account whether their main objectives are social or academic, or both, and what type of congruence can enhance intended outcomes. In this study, mentors focused more on academic (e.g. discussing science topics) rather than social outcomes for their mentees (e.g. discussing science careers); thus, they would have benefitted from higher levels of cognitive congruence. Programs that emphasize social objectives may not need the aid of an intermediate peer to restore cognitive congruence levels, as university students could focus on discussing further educational and career opportunities in STEM using their knowledge as senior students.

Conclusion and Recommendations for the Implementation of STEM Online Peer Mentoring Programs

This study has investigated mentors’ experiences as part of a pilot STEM mentoring program that matched university students and secondary school students in regional areas. It sought to contribute to the peer mentoring literature by exploring mentors’ strategies – and challenges – when providing STEM mentoring via online communication tools. Our findings suggest that online mentoring programs could be employed to increase students’ engagement in STEM if particular aspects of the mentor-mentee relationship are taken into consideration when designing and implementing these programs. Results indicate two areas of particular relevance to the implementation of university-to-school peer mentoring programs: the use of synchronous and asynchronous communication tools, and the role of intermediate peers.

In this study we differentiated two types of similarity or congruence, and found that some mentors struggled to maintain cognitive congruence. This finding raises questions about what degree of mentor-mentee cognitive similarity is needed to enhance academic outcomes for mentees, as well as the importance of monitoring potential fluctuations in congruence levels as mentoring relationships evolve over time. Whilst the literature emphasizes the importance of cognitive congruence as mentors’ ability to provide explanations suitable to mentees’ cognitive development, it is not clear how mentors recognize this “right level” (Lockspeiser et al. 2008). Further research could investigate what social cognitive mechanisms operate in mentor-mentee interactions, and how mentors employ these to ensure their explanations are appropriate to mentees’ cognitive development. Moreover, given that university students are being trained to become scientists, and not educators or science communicators, they may need initial and ongoing support to ensure they engage in conversations suitable to mentees’ cognitive development. We suggest that, to overcome cognitive congruence issues identified in this study, peers closer to mentors’ and school students’ cognitive development could be included in university-to-school mentoring programs. Teachers’ support could also alleviate issues derived from potential mentor-mentee gaps in shared educational experiences, as they can provide mentors with information on mentees’ learning needs. In the program reported in this paper, mentors were instructed to discuss session topics with teachers; however, we do not know if these interactions happened on a regular basis. STEM online mentoring programs would need to ensure that participating teachers regularly update mentors on relevant science content covered in class.

In this study, communication took place through synchronous online tools that helped mentors establish rapport with mentees. The use of audio and video conferencing tools provided participants with verbal and non-verbal communication cues, not readily accessible in text-based mentoring programs; however, mentors still experienced communication issues that impacted on the development of mentoring relationships. Programs that employ synchronous communication tools need to assess the usage and purpose of the online tools provided to participants, as mentors may lack confidence in the use of more complex tools or may resort back to their face-to-face communication skills strengths. As well as improving opportunities for collaboration, we suggest that a combination of asynchronous and synchronous communication tools may address shortcomings in cognitive congruence establishment, providing mentors with alternative communication strategies to explain and discuss topics in congruent ways. In this study, we provided initial training on online communication strategies to mentors. We suggest that mentees could also receive training on how to communicate with mentors, with the objective of enhancing mentees’ participatory role.

Finally, although technology played a role in the development of the mentor-mentee bond, and affected engagement in specific mentoring weeks, it was not the most challenging aspect for mentors in this study. Rather, difficulties to maintain cognitive congruence between participants, role expectations and external support for the program should be taken into account when designing STEM online mentoring strategies. For instance, mentoring relationships in this study were negatively affected when mentees were not able to attend sessions due to other school commitments, after the school holiday break, or when technological issues made communication difficult. These aspects illustrate the impact of participants’ role expectations as well as factors outside the mentor-mentee relationship, such as the timing of mentoring sessions in relation to university and school calendars (e.g. whether mentoring sessions are aligned with school terms). Role-modelling clear expectations is also critical to the success of mentoring relationships and needs to be better integrated into induction programs for mentors and mentees. We argue that mentees may need more guidance before mentoring starts, for instance, by providing them with examples of questions and activities they can do with their mentor.

Further research could extend findings from this study to other online peer mentoring formats, including programs that incorporate face-to-face and online communication tools. In addition, studies that compare the development of online and face-to-face mentoring relationships in comparable programs (or within iterations of the same program) would shed light on whether patterns of relationship development identified in this study are specific to online environments. In this study, data collected on mentors’ strategies and mentees’ engagement relied on mentors’ self-reports. The analysis of mentoring relationships could be further enriched by incorporating mentees’ perceptions of relationship development and their views on the efficacy of the strategies employed by mentors, as well as the analysis of video-recordings of synchronous mentoring sessions for data triangulation purposes. The use of additional data sources would also mitigate potential limitations associated with the use of mentors’ written reflections (e.g. differences in mentors’ ability to express and describe mentee engagement and/or mentoring strategies). Moreover, the effectiveness of mentors’ strategies to establish and maintain congruence could be further explored from the mentee’s perspective, comparing and contrasting mentors’ and mentees’ perceptions of congruence levels and their impact on the success of mentoring programs. Further studies could also employ larger samples of mentors and mentees to quantitatively assess changes in relationship quality indicators, engagement, and congruence, and the impact of these factors on mentee outcomes.

Mentors in this study self-identified as highly efficacious in their mentoring skills, resulting in increased persistence to successfully engage students in their mentoring sessions. Studies that incorporate mentors with varying levels of self-efficacy may discern variations in mentors’ engagement and perceptions of mentee engagement, with consequences for relationship development. Finally, mentors did not refer to the size of the groups as a challenge in the establishment or maintenance of mentoring relationships, nor did it affect social and cognitive congruence dynamics. Inconsistent attendance alongside students attending a limited number of sessions, however, were disruptive factors for mentors. Further research studies could investigate group mentoring dynamics in online environments, employing observational and longitudinal methodologies to ascertain the development of online mentoring relationships in group settings and their specific challenges.


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Correspondence to Ana Garcia-Melgar.

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Garcia-Melgar, A., Meyers, N. STEM Near Peer Mentoring for Secondary School Students: a Case Study of University Mentors’ Experiences with Online Mentoring. Journal for STEM Educ Res 3, 19–42 (2020).

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  • Near peer mentoring
  • STEM engagement
  • Peer learning relationships
  • Online mentoring