Analyzing Barriers to Mentee Activity in a School-Based Talent Mentoring Program: A Mixed-Method Study

Abstract

Studies on talent development show that attaining expertise relies on long-term active engagement with a domain. Mentoring plays a key role in this, but it usually takes place outside of school in informal mentoring relationships, and research on formal school-based talent development mentoring programs is lacking. In the present research, we examined which factors in a novel school-based Learning Pathway Mentoring program hinder mentees’ active engagement with their domain. Due to a lack of corresponding research, we employed an exploratory sequential mixed-methods design (QUAL → quan). We first explored factors affecting mentees’ engagement from the perspective of 55 mentors in the Learning Pathway Mentoring program. The results of these qualitative analyses served to derive research questions about variables associated with decreased mentee domain activity, which we then studied in auxiliary quantitative analyses based on a sample of 48 mentees of the same program. Our combined analyses suggest that reasons for decreased mentee engagement may, in fact, be very heterogeneous and nuanced. As talent development places a significant demand on mentees in terms of extracurricular engagement, difficulties might occur specifically when mentees are expected to set priorities regarding the implementation of learning activities in their talent domain and simultaneously meet increasing school demands.

1. Introduction

Since the early beginnings of talent development research a hundred years ago, educational scientists have been concerned with the question of how best to support gifted students in fully exploring the height of their potential and developing exceptional skills (Dai, 2020). Group-based support measures such as accelerated classes and extracurricular enrichment programs have since been widely implemented, typically showing low to moderate effects on various academic and motivational outcomes (Kim, 2016; Lipsey & Wilson, 1993; Steenbergen-Hu et al., 2016). Some researchers therefore argue that these traditional measures cannot provide the level of individualized support necessary for the attainment of expertise (Ziegler et al., 2022), instead turning to measures that are able to offer more highly specialized learning opportunities, such as mentoring (Grassinger et al., 2010; Subotnik et al., 2021a).

Mentoring can be defined as a relatively stable dyadic relationship between a more experienced person (mentor) and a less experienced person (mentee), characterized by mutual trust, goodwill, and the common goal of advancement and growth of the mentee (Grassinger et al., 2010). Research on the career paths of eminent individuals, for example, in the domains of academics, music, and sports, shows that mentors played a crucial role in their development (Bloom, 1985; Kiewra et al., 2021; Paik et al., 2018; Roche, 1979). However, mentoring geared towards talent development mostly takes place outside of school, either in informal mentoring relationships or in extracurricular mentoring programs (Little et al., 2010; Stoeger et al., 2017, 2016; Subotnik et al., 2010). While there is a growing body of literature addressing school-based mentoring, most of this research focuses on programs that target academically at-risk students (Bayer et al., 2015; Chan et al., 2013; Jablon & Lyons, 2021; Kanchewa et al., 2021; Kanchewa et al., 2018; Karcher, 2008; Keller & Pryce, 2012; Wood & Mayo-Wilson, 2012). Surprisingly, however, there is little research on school-based mentoring programs that aim to support the development of students’ talent in specific subject domains.

1.1. Mentoring in the Context of Talent Development

Historically, talent development research tends to be characterized by small sample sizes impacting the generalizability of findings and a large proportion of (retrospective) qualitative studies such as interviews or biographic case studies, specifically in the domain of excellence research (Paul & Plucker, 2004; Walsh et al., 2012). In practice, the terms talent and giftedness specifically tend to be used in conjunction or conflation, blurring semantics (Gagné, 1998; Sternberg, 2001). Nonetheless, there is agreement that both terms can be commonly defined as the potential to achieve excellence in a domain, which may be evidenced by high intellectual ability and/or aptitude in a domain (i.e., high cognitive abilities, vast knowledge, and refined skills; (Stoeger et al., 2018; Subotnik et al., 2019a)). In recent years, talent development research has undergone a paradigm shift from a focus on individual traits towards a holistic concept encompassing a trajectory towards excellence, which emerges through fruitful interaction between environmental opportunities and individual characteristics (Bloom, 1985; Dai, 2020; Gagné, 2015; Subotnik et al., 2021b, 2019b; Ziegler, 2005).

Research on the talent trajectories of eminent individuals has since demonstrated that mentors play a key role in providing talented individuals with the guidance and opportunities needed to advance their skills and professional career (Bloom, 1985; Kiewra et al., 2021; Paik et al., 2018; Roche, 1979). While engagement with a talent domain may start out rather playful and time-intensive, purposefully planned learning activities and feedback become increasingly important as individuals progress in their specific domain (Bloom, 1985; Ericsson & Harwell, 2019; Subotnik et al., 2021a). Building on the stages of talent development provided by Bloom (1985), in combination with their research-based Talent Development Megamodel, Subotnik et al. (2021a) characterize the function of mentoring roles and relationships via three developmental stages: stage (1) discovering potential and enhancing passion for a domain via role modeling; stage (2) supporting the development of competence via professionally informed guidance and feedback; and stage (3) helping to shape a professional identity and portfolio towards excellence and innovation via facilitating access to professional networks and opportunities. While talent trajectories and the need for early specialization may vary across talent domains, they can start as early as childhood (e.g., mathematics or gymnastics) and early adolescence and will require increasingly specialized mentors throughout (Stoeger et al., 2024; Subotnik et al., 2021a). Subotnik et al. (2021a) argue in favor of the importance of providing specialized mentors specifically during stage 2, i.e., when talented adolescent and young adult students start building the skillsets necessary for moving towards expertise. Establishing formal academic mentoring programs is therefore an interesting alternative to traditional support measures for talented youths.

1.2. Mentoring Programs for Talented Youth: Insights and Opportunities

Extant research on the effects of mentoring programs for talented youth has primarily focused on extracurricular settings or case studies of customized interventions. These studies suggest mentoring interventions not only foster academic skill development but also enhance intrinsic motivation, domain-specific activities, self-regulated learning strategies, certainty about career goals, and a positive self-concept towards critical thinking and research skills (Hébert & Olenchak, 2000; Little et al., 2010; Mammadov & Topçu, 2014; Stoeger et al., 2017, 2016; Subotnik et al., 2010). These motivational, cognitive, and metacognitive aspects have been shown to be crucial for academic achievement as well as vocational choices, including populations of talented students (Heilbronner, 2011; Putwain et al., 2019; Schick & Phillipson, 2009; Schneider & Preckel, 2017; Steinmayr & Spinath, 2009). The advantages of providing talented students with mentors lie specifically in the opportunity of tailoring highly individualized learning goals and experiences independent of school curricula (Gagné, 2015), which can take individual strengths and interests into account (e.g., Catalyst Program; Subotnik et al., 2010), and has even been demonstrated to reverse underachievement in talented students (Emerick, 1992; Hébert & Olenchak, 2000). Although these findings speak to the benefits of mentoring for promoting talent development in youth, studies on school-based mentoring programs that target talented youth are missing.

School-based mentoring refers to mentoring programs where mentors and mentees meet in the mentees’ school. School-based mentoring programs offer a promising alternative to extracurricular initiatives, reaching a broader range of students (Bayer et al., 2015; Chan et al., 2013), including talented individuals who might otherwise lack access to informal mentoring or specialized programs (Stoeger et al., 2024; Subotnik et al., 2011). Moreover, talented children and adolescents obtain most of their early academic experiences in schools as their central learning environment (Coleman et al., 2015). While limited research exists on the effects of school-based mentoring for talented youth, studies with other target groups (e.g., students transitioning to middle school or at-risk student populations) highlight its potential benefits for various academic, motivational, and relational outcomes. These include improvements in academic attitude and performance, self-esteem, self-regulated learning, school engagement, prosocial behavior, and social relationships, as well as reductions in peer victimization for bullied students (Bayer et al., 2015; Chan et al., 2013; Craig et al., 2016; Elledge et al., 2010; Herrera et al., 2011; Kanchewa et al., 2018; Karcher, 2008; Lyons et al., 2019; Martins et al., 2024; Núñez et al., 2013; Simoes & Alarcao, 2014).

In the context of talent development stages, as outlined by Subotnik et al. (2021a), school-based mentoring programs could aim to facilitate initial domain exploration (stage 1) or foster advanced skill acquisition (stage 2). The accessibility and structure of school-based mentoring make it particularly effective for stage 2, a period that demands tailored guidance and deep domain knowledge on the mentor’s side (Stoeger et al., 2024; Subotnik et al., 2021a). Teachers who are trained to support skill development in a specific domain are well positioned to act as mentors, offering targeted support and connecting mentees with more advanced opportunities as needed. Thus, school-based mentoring has the potential to play a vital role in fostering the growth and achievement of talented students within structured and accessible settings.

1.3. General Considerations About Ensuring Effectiveness in School-Based Talent Mentoring

Although much speaks to the benefits of implementing school-based mentoring programs for talent development, research from other mentoring contexts also calls for caution. Recently termed the mentoring paradox (Ziegler et al., 2021), contradictory findings on youth mentoring effects point to the fact that mentoring can be a highly effective support measure for youths,; however, their effectiveness may be easily hampered by a variety of impeding factors on program-level and participant-level scales (Christensen et al., 2020; DuBois et al., 2002, 2011; Herrera & Karcher, 2014; Laco & Johnson, 2019; Raposa et al., 2019). Evidence-based recommendations underline the importance of clearly defined program goals, specified requirements regarding the target group to ensure a good mentee-program-fit, sufficient duration (>1 year), a good fit between the mentor’s professional background and the program’s goals, ongoing training and support for mentors, matching mentees and mentors based on similar interests, frequent mentor–mentee contact and structured mentoring activities, and the monitoring of program implementation (Christensen et al., 2020; DuBois et al., 2002, 2011; Grossman et al., 2012; J. B. Grossman & Rhodes, 2002; Herrera et al., 2011; Karcher et al., 2005; Kupersmidt et al., 2017; McQuillin & Lyons, 2021; Raposa et al., 2019; Spencer, 2006). While extensive research in the field of youth mentoring yields valuable insights into the general conditions needed for successful program implementation, meta-analyses do not typically include talented youth as a specified target group nor talent development as a central goal (e.g., Christensen et al., 2020; Eby et al., 2008, 2013; Raposa et al., 2019; Wood & Mayo-Wilson, 2012). Therefore, any evidence-based considerations regarding the implementation and potential effects of formal school-based talent mentoring programs require drawing from two additional research avenues: first, research on mentoring programs that focus on talent and career development but usually target adult mentees in academic or work settings (Barrett et al., 2017; Eby et al., 2008; Ghosh, 2014; Hamlin & Sage, 2011; Keller et al., 2014), and second, studies concerned with the needs and trajectories of talented individuals (Debatin et al., 2023; Ericsson & Harwell, 2019).

Previous research in the field of academic and career mentoring have largely focused on relational key factors, i.e., the interaction and relationship between the mentor and mentee. As per the mentoring model by Hamlin and Sage (2011), mentoring outcomes depend on both mentee and mentor characteristics and behavior as well as their interaction and experiences. In academic, formal, or professional mentoring contexts, a general willingness to engage with each other (i.e., scheduling regular meetings), regular and respectful interactions, structured activities, and shared interests, priorities, and goals have been identified as conducive relational aspects, while a lack thereof is considered detrimental (Barrett et al., 2017; DuBois et al., 2002; Ghosh, 2014; Keller et al., 2014; Lester et al., 2019). Qualitative research such as mentee and mentor interviews in the context of workplace or academic mentoring also point to the importance of several overarching key aspects and expectations for mentees: initiative, commitment, amount of self-disclosure, organizational skills, and engagement (Barrett et al., 2017; Hamlin & Sage, 2011; Taylor & Black, 2018).

While this research was conducted in other mentoring contexts, several studies point to mentee engagement being a crucial key to success in a talent mentoring program. In line with evidence-based talent development frameworks (Gagné, 2015; Grassinger et al., 2010; Subotnik et al., 2021a), the extent of mentoring benefits will naturally not only depend on students having access to an opportunity such as mentoring but also how they make use of it. Student engagement can be defined as ‘students being actively involved in their learning tasks and activities’ (Lei et al., 2018, p. 517). It typically encompasses motivational and behavioral variables, such as proactivity and responsibility towards projects, high motivation and interest, preparedness for meetings, or openness towards the mentor’s ideas in the context of academic and career mentoring (Barrett et al., 2017; Hamlin & Sage, 2011; Taylor & Black, 2018). Engagement is often investigated as a predictor of academic achievement and performance in secondary and tertiary education (Fisher et al., 2018; Putwain et al., 2019; Tsay et al., 2020) and has also been studied in the context of the underachievement of gifted students and dropouts (Landis & Reschly, 2013). Specifically in the later stages of talent development, professionalized engagement through tailored, domain-specific activities and individualized feedback becomes increasingly important for improving performance and fostering expertise (Bloom, 1985; Ericsson & Harwell, 2019; Subotnik et al., 2021a). Research on expert performance further emphasizes that the time devoted to domain-specific activities aimed at purposefully improving current performance is a key prerequisite for developing exceptional skills (Debatin et al., 2023; Ericsson & Harwell, 2019). In summary, school-based mentoring programs designed to support youth talent development during stage 2 should therefore focus on building on students’ existing passion for a domain (stage 1) by encouraging deeper engagement through tailored activities and facilitating the acquisition of advanced knowledge and technical mastery needed to move towards excellence (stage 3).

An example of a program that applies the outlined considerations for effective talent development mentoring is the Learning Pathway Mentoring program (Quarda et al., 2023; Stoeger et al., 2021; Ziegler et al., 2022).

2. The Learning Pathway Mentoring Program

The Learning Pathway Mentoring (LPM) program is a school-based talent development program focused on supporting talented secondary school students (grades 5–13) in various academic domains (Quarda et al., 2023; Stoeger et al., 2021; Ziegler et al., 2022). Based on Bloom’s early model of talent development (Bloom, 1985) and more recent adaptations (Stoeger et al., 2024; Subotnik et al., 2021a), the LPM program targets students at stage 2 of talent development. Thus, it aims at supporting talented students as they start building the skillset necessary for moving towards expertise by planning, implementing, and continuously adapting individualized learning pathways (Quarda et al., 2023; Ziegler et al., 2022). Identification procedures in talent development programs tailored to stages 2 to 3 should emphasize prior domain-specific achievement and a high domain-specific motivation over general ability (Subotnik et al., 2021a). Thus, the LPM program specifically targets students (mentees) who, in comparison to their peers, are extremely passionate about a certain domain (e.g., demonstrate a high skill level and high motivation for physics) and strive to refine their skills and knowledge within it. The program aims to increase or sustain mentees’ purposeful learning activities in a domain by providing them with a mentor who helps them work towards a challenging long-term goal.

The program’s pilot phase ran from the school year 2019/2020 to 2022/2023 at 27 German secondary schools. Schools that took part in the program could nominate up to six mentees who were subsequently mentored for at least one up to three years within their chosen subject area (talent domain) by a teacher from their school who teaches the respective subject (mentors). Teachers were chosen as mentors because they not only had the necessary domain-specific expertise but also were pedagogically and didactically trained professionals. Schools and future mentors received recommendations on what to look for in potential mentees as per the program aims but were allowed to implement nomination processes as they saw fit. The program defines the responsibilities of mentors and mentees as follows:

Mentees are expected to first define an overarching long-term goal they want to work towards between weekly mentoring sessions (e.g., developing and building a flight machine employing the Magnus effect). Based on this long-term goal and mentees’ individual interests, learning prerequisites, and resources, mentors support mentees in planning their learning pathways by helping derive appropriate milestones (e.g., constructing flight machine parts) and learning activities (e.g., familiarize themselves with specific 3D-modeling software). During mentoring, the mentee and mentor commit to regular meetings of at least 45 to 60 min, where they reflect on the interim progress, discuss current and potential barriers to the implementation of learning activities, and potentially adjust the learning pathway (e.g., adjust milestones based on previous experiences and available resources). Aside from academic and socio-emotional support, the mentor’s role comprises providing regular feedback on learning activities, supporting mentee’s self-regulated learning by encouraging them to keep activities focused on goals and helping them identify and access resources needed for successful achievement (e.g., establishing contact with experts and helping students gain access to university courses or work placements at specialized institutions).

Mentors received ongoing support from program staff and ample networking opportunities with other mentors serving in the program (including monthly online meetings), as well as two-day mentor training annually. Mentor training regularly highlighted mentees and their individual strengths, interests, goals, and activities as the focal point of the LPM program, encouraging mentors to move from a directing teacher role towards a guiding mentor role and to reflect upon differences. Training sessions further included topics such as introduction to materials, identifying and setting appropriate goals, identifying and managing individual interests and resources, effective vs. ineffective mentoring, types of mentoring relationships and support, guidelines for effective feedback and communication, discussion of current experiences and challenges, and best practice examples. Participants were further provided with supporting materials and printouts to structure the mentoring process. These included a wide range of diagnostic material focused on helping the mentor identify extant interests and skills, available resources, and typical schedules and activities of mentees. Mentors also received conversation guidelines for introduction and mid-term review meetings (e.g., guiding questions about mutual expectations towards the mentoring relationship and communication) and documentation sheets for weekly meetings with reflection prompts for their mentee (e.g., “Which resources do you need to achieve your goal?”).

3. Current Study

Mentoring has emerged as a promising approach to guide talented individuals through the developmental stages necessary for achieving expertise (Gagné, 2015; Grassinger et al., 2010; Subotnik et al., 2021a), but it traditionally occurs in informal or extracurricular settings (Little et al., 2010; Stoeger et al., 2017, 2016; Subotnik et al., 2010). Establishing formal school-based mentoring programs presents a valuable opportunity, as they can both increase accessibility for talented youth (Stoeger et al., 2024; Subotnik et al., 2011), and offer targeted support, especially during stage 2 of talent development, when specialized mentors and focused engagement with a domain become increasingly essential (Stoeger et al., 2024; Subotnik et al., 2021a). Research from career or academic mentoring suggests that, from mentors’ perspectives, the individual proactivity, motivation, and engagement of mentees play an important role in mentoring success, while behaviors reflecting a lack of mentee engagement and motivation are reported as detrimental in mentoring (Barrett et al., 2017; Hamlin & Sage, 2011). Additionally, theoretical conceptualizations of talent development, as well as research on predictors of academic success in student and gifted populations, emphasize the importance of active engagement in learning-related activities (Gagné, 2010; Heilbronner, 2011; Landis & Reschly, 2013; Putwain et al., 2019; Schick & Phillipson, 2009; Schneider & Preckel, 2017; Steinmayr & Spinath, 2009; Subotnik et al., 2021a; Ziegler, 2005).

In the present research, we aim to identify barriers to mentees’ active engagement in a school-based talent development mentoring program. Identifying such barriers is an important first step in developing recommendations for programs seeking to maximize mentoring success. As research into school-based talent development mentoring programs and their effectiveness is lacking, we employed a sequential exploratory mixed-methods design (QUAL → quan; Creswell et al., 2003), consisting of a qualitative core study and a supplementary quantitative study. First, in our qualitative core study, we aimed to identify the barriers to mentees’ active engagement within their talent domains as reported by teachers who serve as mentors in a school-based talent mentoring program. Mentors’ reports were chosen as they are the closest and most informed observers of their mentees’ talent development. Thus, our exploratory research question was as follows:

Which barriers to mentees’ active engagement within their talent domains do teachers report who serve as mentors in a school-based talent mentoring program?

Second, based on our qualitative results regarding the factors identified from mentors’ perspectives, we derived research questions about variables associated with decreased mentee engagement and explored the extent to which our qualitative findings would be substantiated or contrasted by quantitative findings from a related mentee sample. Triangulating different sources is an established practice in educational research and offers the advantage of gaining a more holistic understanding of complex processes (Mathison, 1988; Turner et al., 2017), such as mentoring (Drouin et al., 2015). Figure 1 provides an overview of the study design.

4. Qualitative Study

4.1. Method

4.1.1. Sample and Procedure

Mentors in the Learning Pathway Mentoring program are required to conduct a mid-term review with their mentees midway (after approximately 1.5 years) through the targeted three-year mentoring period. To prepare for the mid-term review, mentors are asked to reflect in written form upon different aspects of the previous course of the mentoring (e.g., mentee’s development, available resources, positive aspects, and difficulties encountered in the mentoring process) by means of a questionnaire. From January to March 2021, we contacted all mentors who, as per school contact partners’ reports, were marked as still currently active within the mentoring program (N = 85) and asked them to provide a copy of their questionnaire. A total of 55 mentors (51% male) complied with the request, resulting in a sample of questionnaires relating to 57 mentees (47% male; two mentors within this subsample each supervised two mentees in one-on-one mentoring). As per original nomination questionnaires provided by the schools, which were retroactively matched to mentor reports, these mentors had an average of M = 12.9 years of teaching experience (SD = 8.8; range 0–35) at the beginning of the mentoring program, while their mentees’ age ranged from 10 to 16 years (M = 12.98, SD = 1.42; grade 5–10).

4.1.2. Measures

In order to identify factors that may impede mentoring success and hinder mentees’ active engagement with their talent domains, we analyzed mentors’ answers to three items from the mentor questionnaire: (a) difficulties in the implemented activities (‘Activities: Where did you encounter difficulties?’), (b) difficulties in communication and cooperation (‘Where did you encounter difficulties in communication and cooperation [between you and your mentee]?’), and (c) changes in resources (‘How have the available resources changed throughout the course of mentoring?’). Mentors provided their answers to these open-ended questions in written form; subsequently, their reports were categorized using content analysis. An overview of all steps is included in

Table 1. Overview of steps in qualitative analysis of mentor reports.

Step	Description	Sample
(1)	Collection of answers in mentor reports to the following: (a) Activities: Where did you encounter difficulties? (b) Where did you encounter difficulties in communication and cooperation [between you and your mentee]? (c) How have the available resources changed throughout the course of mentoring?	N = 57 mentor reports
(2)	Descriptive data analysis of word count in mentor replies.	N = 57 mentor reports
(3)	Two coders mark all missing, neutral, and positive statements across all three questions, e.g., (a) “activities have not concluded yet”; (b) “none; there were no difficulties in communication, the cooperation was excellent; reliable, dependable”; (c) “no change”.	N = 57 mentor reports
(4)	Removal of all cases lacking any report of difficulties (n = 11).	N = 46 mentor reports
(5)	Sample description by gender (mentor, mentee) and mentoring subject based on matched pre-mentoring nomination questionnaires.	N = 46 mentor reports
(6)	Aggregation of answers reporting difficulties across questions per case.	N = 46 mentor reports
(7)	Isolation of text segments for coding. Example: k1: Corona, k2: contact only via telephone/online, k3: little time of mentee, k4: due to other hobbies/interests.	N = 46 mentor report K = 129 text segments
(8)	The main author applies inductive in vivo coding over several iterations, identifying ten recurring categories (0–9).	N = 46 mentor reports K = 129 text segments C = 10 categories
(9)	The main author develops a codebook with explanations and a test coding sheet with fictitious text samples for the training unit (45 min).	NT = 4 KT = 17
(10)	Coding of text segments by two trained independent coders (Krippendorff’s alpha = .81). Inconsistencies (k = 25) are discussed with the main author and jointly recoded.	N = 46 mentor reports K = 129 text segments C = 10 categories
(11)	Frequency count of categories per text segments (k) and over mentor reports (n).	N = 46 mentor reports K = 129 text segments C = 10 categories
(12)	Iterative typology classification on core themes of mentoring problems by frequency and source (mentee, relational, and contextual).	N = 46 mentor reports T = 9 types

.

4.1.3. Qualitative Text Analysis

Answers averaged in word count at M = 24.50 words (range 4–66; Mdn = 23.00; 21 missing answers) to question (a), M = 13.11 words (range 1–93; Mdn = 9.00; 12 missing answers) for question (b), and M = 12.02 words (range 1–47; Mdn = 10.00; 14 missing answers) for question (c). As evidenced by these descriptive data, most mentors provided answers in the form of bullet points or elliptical phrases.

As a first step in data preparation, the main author and a second independent researcher of the same department marked down all text material within this sample that could be excluded from further analysis as per the following criteria: missing, positive, or neutral statements (e.g., ‘Nextcloud was implemented and works well’.). Both coders then compared and discussed ambiguities within their results until they reached a consensus. Through this process, they identified eleven cases where mentors did not report any difficulties whatsoever, leaving 46 mentor reports providing descriptions of factors impeding their mentoring process. These reports were related to n = 26 female and n = 20 male mentees (for a detailed description of the sample selected for qualitative data analysis, see

Table 2. Mentor–mentee sample represented in mentor reports after data selection (N = 46).

Domain		Mentor		Mentee
	n	Female	Male	n	Female	Male	%
Mathematics	16	6	10	16	5	11	34.78
German	11	8	3	11	9	2	23.91
Biology	6	5	1	6	3	3	13.04
History	7	4	3	8	6	2	17.39
Physics	2	0	2	2	1	1	4.35
Technics	1	0	1	1	0	1	2.17
Geography	1	1	0	1	1	0	2.17
English	1	1	0	1	1	0	2.17
N	45	25	20	46	26	20
%		55.56	44.44		56.52	43.48	100

).

We initially planned to conduct separate content analyses for each item, but a precursory review and markdown of the data revealed that mentors often did not differentiate between items, as evidenced by the usage of similar keywords or phrases across items and mentors. For example, some mentors mentioned aspects referring to resources or communication (e.g., ‘irregular contact’, ‘upholding contact was on me’, ‘insufficient technical equipment’) when answering the first question about difficulties in the implemented activities. In four cases, mentors even gave a complete description of all encountered difficulties in mentoring in answer to the first question and subsequently noted down short reference indicators such as ‘see above’ when asked about communication and cooperation aspects or changes in resources. We therefore decided to aggregate all answers to the three items of interest per case before encoding statements as part of the subsequent content analysis.

The main author then derived in vivo codes for the remaining text segments in an inductive manner over several iterations using MAXQDA 2022. In order to increase objectivity and to ensure the discriminatory power of these categories, two department members independently recoded the same text segments following a codebook developed by the main author after receiving a short digital training unit (45 min) including seventeen fictive text segments (see supplementary material for codebook and training examples). Intercoder reliability was then computed in R (R Core Team, 2022).

4.2. Results

4.2.1. Themes and Frequencies

Ten key themes covering 129 text segments emerged (see

Table 3. Encoded text segments (k) per category, overall, and by n out of N = 46 mentor reports.

Category	Description	k	n	Examples
(1)	Mentee’s inconsistent motivation and interest(s)	20	19	‘sustaining motivation for work’; ‘due to other hobbies/interests’
(2)	Mentee’s insufficient willingness or ability to invest time	13	12	‘time expenditure was too high for her’; ‘mentee has little time’
(3)	Mentee’s low commitment to tasks, goals, or agreements	13	9	‘continuity and effort’; ‘non-binding nature of some agreements on the student’s side’
(4)	Mentee’s low initiative	14	11	‘many ideas/suggestions came from the mentor, not mentee’; ‘no or low initiative of mentee’
(5)	Mentee’s inability to work autonomously	6	6	‘there were difficulties regarding the independent implementation by mentee’; ‘practical implementation was often difficult for him’
(6)	Mentee–mentor interaction (quality)	7	7	‘video conferences too impersonal’; ‘contact only via telephone/online’
(7)	Mentee–mentor interaction (quantity)	10	10	‘frequent changes in the lesson plan, fixed scheduling not possible’; ‘meetings were partly irregular’
(8)	Problems choosing appropriate goals and activities	13	9	‘objective was […] not sufficiently clear’; ‘short-term goals were often too difficult’
(9)	COVID-19 pandemic	25	24	‘especially during lockdown’; ‘due to Corona’
(0)	Statements relating to resources	8	6	‘technical resources were lacking’; ‘general conditions and the social components have changed due to the [mentee’s] illness’

), ranging from 1 to 7 uniquely codable text segments per mentor report. The analysis yielded sufficient intercoder reliability between both independent coders (Krippendorff’s alpha = .81) and was marginally increased by including coding provided by the main author (Krippendorff’s alpha = .85). Inconsistencies in coding pertained to 25 text segments. In a joint video conference, coders discussed these inconsistencies until they could match each text segment with a single code. In some cases, multiple text segments within a mentor report represented the same category. Therefore, we created an additional variable in which the presence of each category in a mentor report was counted a maximum of one time to obtain an accurate proportional representation of themes mentioned across all mentee–mentor dyads. Table 3 provides an overview of total instances (k text segments per coding category) and a number of mentor reports (n) in which at least one text segment matching the respective category was included.

About half of all categorized text segments were coded as categories describing impeding factors related to mentee motivation and behavior, namely mentee’s inconsistent motivation and interest, insufficient willingness or ability to invest time, low commitment to tasks, goals, or agreement, and aspects of self-direction such as low initiative, and inability to work autonomously (categories 1 to 5, k = 57). Another quarter of text segments were coded as categories referring to impedimental relational aspects, i.e., quality of interaction, quantity of interaction, and problems choosing appropriate goals and activities (categories 6 to 8, k = 30). The remaining text segments had been coded as either the category for statements referring to the COVID-19 pandemic (category 9, k = 25) or as the category for statements describing issues with resources (category 0, k = 8). Unsurprisingly, text segments regarding the pandemic situation led to the overall ranking across categories (k = 25). At the time of data collection in early 2021, recurring partial school lockdowns and ensuing organizational difficulties surrounding quarantine protocols and infrastructure for hybrid or online classes still heavily affected German schools and thus in part also the Learning Pathway Mentoring program.

In total, about a quarter of mentors in our complete sample (n = 11) made statements concerning the quality or frequency of mentee–mentor interaction (category 6 or 7) in relation to the COVID-19 pandemic (9), such as ‘(7) partially unable to communicate with mentee (9) during lockdown’. We found a similar pattern for statements about resources (n = 6, category 0). Four mentors commented on either insufficient technical equipment, presumably to ensure a continuation of mentoring sessions via video conference tools or reduced social support for mentees, as in ‘mentee is struggling with lockdown due to lack of social contacts’. The remaining two segments referred to recurring or long-term illness of their mentee. Barring the theme of the pandemic, the second most frequently counted category was the one representing statement relating to an inconsistent motivation or interest of the mentee (category 1, k = 20). Since this finding particularly differed from existing mentoring research, we decided to investigate whether difficulties in activities as reported by mentors could be further classified in the form of a problem-related typology.

4.2.2. Typology Investigation

For the problem typology investigation, we conducted an iterative case-by-case investigation of all mentor reports, starting with the first five categories (mentee-related) and moving on to relational and contextual aspects. Resulting types and core themes are displayed in

Table 4. Problem typology (N = 46 mentees).

Types	Core Theme	n	Examples
Mentee
(1a)	Inconsistent motivation	11	‘intermittently questionable motivation’; ‘long-term motivation’
(1b)	Inconsistent interest in domain subject	2	‘he lost interest in the subject a little’; ‘mentee has shifted her field of interest from biology to computer science’
(2)	Time constraints	6	‘already 5–6 h of lessons online daily’; ‘high workload in senior classes’
(2 + 1b)	Time constraints due to concurrent interests	5	‘little time of mentee due to other hobbies/interests’; ‘Student does not invest a sufficient amount of time. Has lots of additional projects (music).’
(2 + 1a)	Time constraints and low motivation	1	‘maintaining motivation to work’, ‘daily demands’
(3, 4, 5)	Low self-direction, responsibility, commitment	8	‘mentee has great difficulty with independent work organization, [she] rarely used the school platform’
Relational
(6/7 + 9)	Inhibited interaction during lockdown	6	‘meetings/appointments did not always work out due to corona conditions.’
(8)	Unsuitable goals/activities	3	‘finding suitable mid-term goals/topic areas that lead to the long-term goal’
Contextual
(9)	Practical difficulties relating to the pandemic	4	‘interviews with contemporary witnesses (over 80 years old) under the conditions of contact restrictions (“Corona”)’

.

As mentioned above, the most frequently counted mentee-related category represented statements related to the mentee’s inconsistent motivation or interest(s). Overall, almost half of all mentor reports contained at least one text segment matching this category (n = 19). We found that statements referred to one of two core themes: (1a) mentee’s inconsistent motivation or (1b) inconsistent interest(s). We further discovered that about one-third of these mentor reports occurred in conjunction with statements reflecting time constraints (category 2). Therefore, we additionally included all mentor reports in the first step of our typology investigation that contained at least one text segment that was coded as category 2. This resulted in twenty-five mentor reports, of which eleven could be classified as type (1a) inconsistent motivation only, two as type (1b) inconsistent interest only, and six as type (2) time constraints only. Additionally, five were classified as type (2 + 1b), representing time constraints due to concurrent interests, and one as type (2 + 1a), representing time constraints and low motivation. We found that in all six ‘time constraints only’ type cases (2), mentors exclusively referred to school workload. Overall, in eleven out of twelve mentor reports mentioning time constraints, mentors specifically made additional statements suggesting that working on their mentoring projects within their domain was not always their mentees’ priority because of either other interests and engagements (2 + 1b) or school workload (2) taking up their mentees’ time. This revealed that mentors of these mentees did not necessarily associate an insufficient engagement of mentees with an overall inconsistent motivation (1a) or inconsistent interest in the domain subject (1b), which seemed to be the case for the remaining 14 mentees within this subsample. Additionally, in 11 of the 25 classified mentor reports, mentors also commented on associated behavioral criteria such as either their mentees’ low initiative (category 4) or low commitment to tasks, goals, or agreements (category 3).

This left 21 unclassified mentor reports in which none of the previously described core themes representing factors related to mentee interest or motivation were present in the form of coded categories. In the second step of our typology investigation, we classified eight of those mentor reports as types (3, 4, 5), representing a summative core theme of low self-direction, responsibility, and/or commitment of mentees. Difficulties in activities as described by mentors related to mentees demonstrating a lack of ability to work in an autonomous manner (category 5), not taking initiative within their projects (4), or not committing to tasks, goals, or agreements (3). For instance, one mentor described it as having to ‘push quite intensively’, another as ‘In the beginning, there were difficulties because the mentee started the mentoring with the idea that “you’re going to explain all my questions to me now”.’ Some mentors communicated that their mentee’s low self-direction or initiative exacerbated issues regarding regular interaction with their mentee and progress of mentoring activities, especially during the lockdown, e.g., ‘mentee has great difficulty with independent work organization, [she] rarely used the school platform’.

In the last step of our typology investigation, we took a closer look at the remaining mentor reports, where coded text segments covered only categories 6 to 9 and 0. We found that core themes could be classified as either relational, as in reduced quality or quantity of interaction (category 6 and 7) and finding appropriate goals and activities (category 8), or contextual, as in external influences such as the COVID-19 pandemic (category 9) or lack of resources (0). For three cases, difficulties described in mentor reports pertained to a single relational core theme, representing unsuitable goals or activities (type 8), as mentor reports only referred to difficulties in finding appropriate or effective mentoring goals or fitting tasks to their mentee’s skill level (category 8). For the remaining ten mentor reports, we found that six specifically described that school lockdowns (category 9) affected the quantity or the quality of interactions with their mentee (category 6 or 7), in turn hampering the progress of mentoring project activities or affecting relationship quality. We classified these cases as relational, with a core theme of inhibited interaction during lockdown (type 6/7 + 9; e.g., ‘meetings/appointments did not always work out due to corona conditions’; ‘video conferences too impersonal’). The remaining cases could be classified as contextual and COVID-19 only, as coded categories in those mentor reports merely related to a core theme of practical difficulties associated with the COVID-19 pandemic (type 9, n = 4). All statements relating to resources (category 0) occurred in conjunction with other categories in differently classified cases, therefore not resulting in a separate type.

5. Quantitative Study

As research on factors that may impede mentoring success in school-based mentoring programs aiming to support youths’ talent development is lacking, the main purpose of our qualitative study was to identify factors that may be associated with decreased engagement of mentees within their talent domain. From mentors’ perspective, three major themes emerged: First, mentees being unable or unwilling to invest the amount of time needed for mentoring activities; second, an overall decrease in motivation and/or interest of mentees; and third, mentees’ desire to (instead) prioritize school work or other interests. However, regardless of their expertise, mentors may be influenced by subjective biases or implicit theories when giving their reports. Thus, we supplemented our qualitative findings with auxiliary analyses based on a related sample of Learning Pathway mentees. More precisely, we sought to explore whether the extent to which mentees prioritized their talent domain over other activities was associated with the amount of time mentees reported investing in their talent domain and whether changes in the priority mentees assigned to their talent domain and changes in their interest for the talent domain, predicted mentee engagement after two years of mentoring. In line with the aims of the LPM program, we operationalized mentee engagement as the extent of mentees’ activities in their talent domain. Thus, the auxiliary analyses comprised the following exploratory research questions:

• Is there a positive correlation between the priority mentees assign to their talent domain (domain priority) and the time they invest in talent domain activities (domain time investment)?
• Do changes in the priority mentees assign to their talent domain (domain priority) over the course of the mentoring program predict the extent of mentees’ talent domain activities (domain activity) after two years of mentoring?
• Do changes in mentees’ interest for their talent domain (domain interest) over the course of the mentoring program predict the extent of mentees’ talent domain activities (domain activity) after two years of mentoring?

In addition, we investigated the impact of the COVID-19 pandemic on mentees’ active engagement since it represented the most stated category within our qualitative sample, resulting in an additional fourth research question:

4.

Do mentee–mentor contact interruptions due to the COVID-19 pandemic negatively predict the extent of mentees’ talent domain activities (domain activity) after two years of mentoring?

5.1. Method

5.1.1. Procedure

Data collection took place in mentees’ schools. Mentees filled out a questionnaire before mentoring started (August/September 2019), after one year of mentoring (June/July 2020), and after two years of mentoring (June/July 2021). However, pandemic-related restrictions heavily affected participation, specifically for the mid-measurement point, to the extent that data collection had to be aborted. Thus, we focus our analyses on two measurement points (pre-mentoring after two years of mentoring). Additionally, we extracted one variable from a separate mentor survey, which focused on the impact of COVID-19 and was conducted in October 2020.

5.1.2. Sample

A total of 82 mentees filled out the pre-mentoring mentee questionnaire, and 48 mentees (59%) filled out the mentee questionnaire at both measurement points (pre-mentoring, after two years of mentoring). Results are reported based on the sample of 48 mentees (M_age = 13.17, SD = 1.40, 22 males) for whom data are available for both measurement points (for baseline comparisons of the pre-mentoring sample, dropouts, and study sample, see

Table 5. Means, standard deviations, and sample sizes across measurement points and subsamples.

Sample	All Cases a (N = 82)	Dropouts (n = 34)	Study Sample (n = 48)	Independent Samples t-Test Results (Dropout vs. Study Sample) b
Variable	M (SD)	M (SD)	M (SD)	t	p
Pre-mentoring
Age	13.15 (1.45)	13.12 (1.53)	13.17 (1.40)	0.150	.881
GPA	5.14 (0.65)	5.05 (0.63)	5.20 (0.66)	1.027	.308
Domain activity	4.32 (1.10)	4.05 (1.28)	4.50 (0.92)	1.775	.064
Domain priority	4.83 (1.01)	4.53 (1.19)	5.04 (0.81)	2.173	c .034
Domain interest	5.14 (0.68)	4.87 (0.64)	5.33 (0.65)	3.175	.002
After two years
Weekly hours invested in domain			6.63 (3.48)
GPA			5.28 (0.69)
Domain activity			4.61 (0.87)
Domain priority			4.77 (0.87)
Domain interest			5.10 (0.69)

Note. Age = mentee age; GPA = grade point average in main subjects Math, German, and first foreign language; ^a Two mentees did not complete the pre-mentoring questionnaire; ^b Effect sizes for significant differences entail Cohen’s d = 0.52, 95% confidence interval [0.07, 0.96], for pre-mentoring domain priority and d = 0.71, 95% CI [0.26, 1.16], for pre-mentoring domain interest; ^c Adjusted p-value for two-tailed testing with unequal variances.

). Mentees participated in mentoring in one of the following talent domains: Mathematics, German, biology, history, physics, geography, and technics. In subsequent analyses, data were aggregated across all talent domains due to highly unequal or low sample sizes across domain subjects (e.g., corresponding expected values for Chi-Square tests < 5). Mentees were clustered across 20 schools, with cluster sizes ranging from one to six per school and an average cluster size of 2.4.

5.1.3. Measures

GPA. GPA was computed as an average score across grades in the three main subjects of the German educational system (i.e., German, mathematics, and first foreign language), ranging from 1 (best grade) to 6 (worst grade). GPA was recoded to have a score of 6, reflecting high achievement.

Domain activity. We assessed the extent of mentees’ activities in their talent domain at both measurement points with an adapted version of a semantic differential scale developed to measure the frequency of STEM activities (see Stoeger et al., 2019). The scale represents activities related to habitual information-seeking behavior and research activities. Items were modified by substituting ‘STEM’ with the respective talent domain, e.g., mathematics. On a 5-item Likert scale, scale points ranged from 1 (never) to 6 (very frequently). A sample item reads, ‘I never/very frequently engage in math activities in my spare time’. The adapted version retained acceptable reliability coefficients at both measurement points (Cronbach’s α = .76 and .77, n = 48).

Domain priority. Domain priority describes the extent to which mentees prioritize their talent domain over other activities or interests. It was measured at both measurement points via a three-item six-point Likert scale, ranging from 1 (strongly disagree) to 6 (strongly agree). A sample item reads: ‘In my spare time, engaging with [talent domain] content is one of my absolute favorite things to do’. The reliability of the scale proved acceptable at both measurement points (Cronbach’s α = .68 and .74, n = 48).

Domain interest. Mentees’ interest in their talent domain was measured at both measurement points with a six-item Likert scale first developed by Ziegler et al. in 1998 (see Stoeger & Ziegler, 2008), with scale points ranging from 1 (strongly disagree) to 6 (strongly agree). The scale operationalizes domain interest as a subjective value of the talent domain via items such as ‘It is important to me to be good at [talent domain]’ or ‘I like the lessons in [talent domain]’. Reliability was satisfactory for both measurement points (Cronbach’s α = .79 and .77, n = 48).

Domain time investment. The time mentees invested in talent domain activities was first assessed as part of the student survey after two years of mentoring, using two metric open format single items combined with a dichotomous item. The first item prompted participants to give an estimate of weekly hours spent on school-specific curricular activities (‘How many hours do you invest in school-related activities in [talent domain] per week?’). Participants were then provided with a dichotomous item relating to additional extracurricular activities (‘Do you spend additional time on activities related to [talent domain] (e.g., voluntary extracurriculars, special courses, attending talks? Yes/No?’), followed by an estimation prompt for these extracurricular activities (‘If yes, how many hours per week?’). Estimates of mentees who reported that they did not engage in any extracurricular activities were manually set as “0 h” for time spent on extracurricular activities.

We could not exclude the possibility that some participants did not sufficiently differentiate between curricular and extracurricular activities, specifically those related to their mentoring projects, which may take place both at and outside of school. We therefore decided to combine hourly estimates for both curricular and extracurricular activities into a common sum variable to obtain an approximate value for the overall self-reported weekly time investment of mentees regarding the individual talent domain.

Continuation during lockdown. The dichotomous variable representing continuation during lockdown stems from a separate mentor survey focused on the impact of COVID-19, conducted in October of 2020: ‘Were you able to continue your mentoring during school lockdowns, albeit in a different format (e.g., video conferences, cloud and platform services, e-mail, calls)? Yes/No’. We subsequently matched mentor responses to corresponding mentees in our sample.

5.1.4. Analysis

We found some minor evidence for systematic variation between school clusters using the lavaan package in R (R Core Team, 2022), specifically for domain activity and domain priority (ICC range [.09; .14] across both measurement points; ICCs_{domain interest} ≤ .01), but based on the recommendations by Lai and Kwok (2015) and subsequent analyses (deff = [1.1; 1.2], number of clusters ≤ 20), refrained from conducting multilevel analyses.

In order to explore whether the findings gathered from our qualitative analysis as per mentor reports (insufficient mentee engagement in mentoring activities due to decreased domain priority, decreased domain interest, insufficient time investment, and mentoring interruptions due to the COVID-19 pandemic) could be substantiated by quantitative analyses in mentee data, we conducted an auxiliary multiple regression analysis. More specifically, we investigated if changes in domain priority and domain interest predict domain activity after two years of mentoring while accounting for pre-mentoring baseline scores (domain activity, domain interest, domain priority) and continuation during lockdown (dummy-coded). All quantitative analyses were computed using SPSS (Version 28).

5.2. Results

5.2.1. Descriptive

Means and standard deviations for the baseline sample (n = 82; 48% male), the dropout sample (n = 34; 53% male), and the study sample (n = 48; 46% male) are presented in Table 5. The study sample and dropout sample did not differ significantly by gender (Χ² (1, N = 82) = 0.40, p = .526) or by pre-mentoring age (t(80) = 0.2, p = .881). They did, however, differ significantly regarding pre-mentoring domain priority and pre-mentoring domain interest (t(45.16) = 2.17, p = .034; t(80) = 3.18, p = .002). Mentees in the dropout sample were furthermore significantly less likely to have continued their mentoring during pandemic school lockdowns (Χ² (1, N = 77) = 4.59, p = .032).

Correlation coefficients for all continuous variables within and across measurement points in the study sample are presented in

Table 6. Bivariate intercorrelations across measurement points in mentee sample (N = 48).

		Age 1	GPA 1	Domain Activity 1	Domain Priority 1	Domain Interest 1	GPA 2	Domain Activity 2	Domain Priority 2	Domain Interest 2
GPA 1	r	−.03	–
	p	a .846
Domain	r	.23	−.10	–
activity 1	p	.116	a .521
Domain	r	.23	−.08	.54	–
priority 1	p	.116	a .583	<.001
Domain	r	−.18	−.02	.33	.30	–
interest 1	p	.223	a .895	.022	.036
GPA 2	r	.05	.73	−.07	−.01	.02	–
	p	.754	a <.001	.652	.955	.874
Domain	r	.34	−.08	.52	.44	.31	−.05	–
activity 2	p	.017	a .593	<.001	.002	.032	.753
Domain	r	.35	−.16	.23	.42	.31	−.14	.64	–
priority 2	p	.016	a .295	.124	.003	.034	.343	<.001
Domain	r	−.04	.08	.07	.21	.51	−.02	.31	.59	–
interest 2	p	.772	a .586	.636	.150	<.001	.901	.030	<.001
Domain	r	.16	−.18	.15	.12	.15	−.11	.35	.51	.24
time 2	p	b .303	c .250	b .328	b .430	b .308	b .476	b .017	b <.001	b .102

Note. ¹ Pre-mentoring, ² After two years of mentoring; domain time = [domain time investment] self-reported hours invested in domain per week; Bold correlation coefficients represent large effects, r ≥ .50; ^a N = 47, ^b N = 46, ^c N = 45.

. Effect sizes were moderate to high within main measures across measurement points (domain activity: r = .52, p < .001; domain priority: r = .42, p = .003; domain interest: r = .51, p < .001). Domain time investment was moderately correlated with domain activity (r = .35, p = .017) and strongly correlated with domain priority (r = .51, p < .001) after two years of mentoring.

5.2.2. Predicting Domain Activity After Two Years of Mentoring

To investigate whether changes in domain priority and domain interest would significantly predict domain activity after two years of mentoring, we calculated two additional change score variables for domain priority (domain priority CS = domain priority² − domain priority¹; M = −0.19, SD = 0.73, N = 47) and domain interest (domain interest CS = domain interest² − domain interest¹; M = −0.23; SD = 0.66, N = 48), which we entered into the multiple regression model. Predictors included these change scores, pre-mentoring scores for domain activity, domain priority, and domain interest, as well as the continuation of mentoring during pandemic school lockdowns as a dummy-coded variable. The data met the assumption of independent errors after the exclusion of outlier data exceeding three standard deviations on variables included in the regression model (Durbin–Watson value = 1.85).

The overall regression was statistically significant (R² = .59, F(6, 38) = 9.13, p < .001). As depicted in

Table 7. Multiple regression model predicting domain activity after two years of mentoring.

Variable	b	95% CI	SE	β	t	p
Constant	0.008	[−1.899, 1.915]	0.942		0.009	.993
Domain activity 1	0.221	[−0.047, 0.488]	0.132	0.240	1.671	.103
Continuation during lockdown	0.419	[−0.013, 0.851]	0.213	0.224	1.966	.057
Domain priority 1	0.692	[0.307, 1.077]	0.190	0.519	3.642	<.001
Domain priority CS	0.412	[0.077, 0.747]	0.165	0.305	2.491	.017
Domain interest 1	−0.018	[−0.382, 0.346]	0.180	−0.014	−0.099	.922
Domain interest CS	−0.164	[−0.525, 0.189]	0.179	−0.124	−0.918	.364

Note. CI = confidence interval; ¹ pre-mentoring score; continuation during lockdown = 1: ‘Yes, mentoring could be continued during lockdown, albeit in a different format’; CS = change score between values at pre-mentoring and after two years of mentoring.

, domain activity after two years of mentoring was significantly predicted by domain priority at baseline (b = 0.69, 95% CI [0.30, 1.07], p < .001), and changes in priority scores over time (domain priority CS, b = 0.41, 95% CI [0.08, 0.74], p = .017), while neither interest (change) scores nor continuation of mentoring during school lockdowns significantly predicted domain activity after two years of mentoring.

6. Discussion

6.1. Summary of Findings

Mentoring is considered one of the best and most effective support methods in terms of individual development and expertise acquisition (Bloom, 1985; Grassinger et al., 2010; Jarvin & Subotnik, 2010; Stoeger et al., 2024; Subotnik et al., 2021a; Ziegler et al., 2022). It stands to reason that schools can also make a substantial contribution to individual talent development through mentoring services. In particular, one can assume that teachers can effectively support talent development and active mentee engagement with a domain through their own expertise in subject domains (Subotnik et al., 2021a). Unfortunately, research on talent mentoring programs for youth is scarce, and existing research is mostly limited to extracurricular programs, which are not integrated into existing educational structures.

Considering the lack of systematic research with regard to school-based talent mentoring, we employed an exploratory sequential mixed method analysis in the context of the novel Learning Pathway Mentoring program. Our research aim was focused on identifying factors that may negatively affect mentee engagement and successful implementation of domain-specific learning activities. In our core qualitative study of mentor reports, we first identified factors that may interfere with mentees engaging in domain activities from the perspective of LPM mentors. An auxiliary quantitative study in a sample of LPM mentees aimed at supplementing and contrasting our qualitative findings regarding mentee engagement via the inclusion of a different data source.

Qualitative content analysis of mentor reports stating difficulties in the implementation of mentoring activities revealed three major sources of interference: (a) mentee-related aspects, such as insufficient motivation, time constraints, or low self-direction, (b) relational aspects, including difficulty in finding appropriate goals or interaction quantity, and (c) environmental influences such as school lockdowns during the COVID-19 pandemic. In the majority of cases (70%), mentors attributed difficulties to mentee-related aspects, followed by the impact of school lockdowns (21%), which, according to mentors, further affected relational aspects such as interaction quality or quantity during mentoring. Regarding mentee-related aspects, dominant factors represented a combination of behavioral (e.g., insufficient time investment) and motivational-affective indicators of engagement (e.g., unfocused interest in the talent domain subject; prioritization of schoolwork or other interests).

About half of the mentor reports suggested that some mentees might have difficulties investing the appropriate amount of time needed for a successful and smooth implementation of their mentoring projects and activities or might not be sufficiently interested in their talent domain. Furthermore, mentors primarily attributed an insufficient time investment to schoolwork or other interests taking priority over the mentoring domain. In order to supplement these findings derived from the mentors’ perspective, we explored whether mentee-reported time investment and mentee-reported domain priority after two years of mentoring were associated in a related mentee sample. Furthermore, we investigated whether changes in mentee-reported domain priority and mentee-reported domain interest predicted mentees’ domain activity after two years of mentoring.

The time investment was indeed strongly correlated with domain priority, explaining about 25% of the variance. We also discovered a positive correlation between time investment and domain activity after two years of mentoring in the medium effect size range. Thus, while we did find positive associations between mentees’ time investment in their talent domain and mentees’ talent domain activities, these associations were not particularly strong. However, this finding is not necessarily surprising. Related research focused on professional athletes or musicians suggests that the development of expertise is not necessarily associated with how much time a person spends on their talent domain overall, but rather how they spend it and make use of it (Bonneville-Roussy & Bouffard, 2015; Debatin et al., 2023; Young & Salmela, 2010). Ericsson et al. (1993) coined the term ‘deliberate practice’, to specifically describe activities within a talent domain purposefully aimed at developing existing skills and correcting deficits.

Surprisingly, neither pre-mentoring interest nor changes in interest over two years of mentoring were predictive of domain activity after two years of mentoring. Our results, however, pointed to domain priority being a deciding factor with regard to mentees’ engagement in domain activity. Baseline domain priority scores as well as domain priority change scores both significantly predicted domain activity in the mentoring subject after two years of mentoring in the regression model. A potential caveat in interpreting these results, above all the null effects concerning mentees’ changes in domain interest in domain activities after two years, is that a potential selection bias cannot be ruled out. Baseline comparisons showed significant differences in pre-mentoring scores between the dropout sample and the study sample, with study sample mentees reporting higher interest and assigning higher priority to their domain. In 56% of cases, dropout sample mentees did not complete the second measurement point because they had voluntarily dropped out of the mentoring program earlier. It is a plausible assumption that a lack of domain interest may have played a substantial role in these pre-mature match closures.

Despite that, quite a few mentors attributed difficulties regarding a mentee’s active engagement to pandemic-related school lockdowns and ensuing difficulties regarding, for example, the technical infrastructure necessary for regular mentee–mentor interaction, the continuation of mentoring during school lockdowns did not significantly predict mentees’ domain activities after two years of mentoring in our supplemental analyses based on related mentee data. This, of course, does not prove that mentees did not reduce their overall mentoring activities during lockdown. Furthermore, it does not suggest that their mentors’ judgment of a relative reduction or disruption of mentoring activities compared to, for example, pre-pandemic times is not accurate. We might carefully assume, however, that if there was an effect, it does not seem to have affected our mentee sample long term, as we found no evidence suggesting as much by our second measurement point (approximately one-year post-school lockdowns). However, as dropout sample mentees were significantly more likely to have suffered a disruption of their mentoring during lockdowns, future research analyzing dropout cases within the LPM program is needed.

6.2. Recommendations for Talent Development Mentoring

Establishing school-based talent development programs in general, and mentoring programs specifically, may prove to be a resource-intensive endeavor since, by definition, only a handful of students may qualify and/or profit. Understandably, schools would be interested in learning how to identify those suitable for such a program. Mentors and/or their respective school administrative teams might nonetheless be tempted to nominate mentees who appear to be well established in terms of scholastic achievement and overall work ethic across all school subjects over those with more concentrated abilities or interests. However, mentees who fit this pattern might, in turn, be less willing to commit to the mentoring in terms of time investment, especially if they place a higher priority on keeping their overall grades up compared to putting all their eggs in one domain basket (C. P. Chen & Wong, 2013; Smith & Wood, 2020). This interpretation would be in line with our qualitative findings, where mentors of some mentees attributed difficulties regarding the implementation of activities to high or increasing school demands of their mentees, as well as mentees not prioritizing their domain or mentoring projects. As Lohman (2009, p. 995) summarized, “Simple schemes that establish an arbitrary cut score on an IQ or achievement test are administratively convenient but identify only a fraction of those who will later attain excellence”.

The LPM program specifically advises mentors and schools to choose mentees who are especially passionate about a specific domain, and our evidence supports that neither GPA nor overall interest in the domain may be a suitable indicator for mentoring success in this talent development context. In fact, only domain priority baseline scores and change scores reflecting priority changes over the course of the mentoring program predicted mentees’ level of domain activity after two years of mentoring in our model. Furthermore, our dropout sample and study sample differed significantly in pre-mentoring priority scores. Taken together, these results indicate that a mismatched mentee-program fit with regard to mentees’ subjective priorities may result in ineffective mentoring or even facilitate mentee dropout (Christensen et al., 2020; DuBois et al., 2002; Hairon et al., 2020). One mentor specifically attributed his mentee dropping out of the program to both time constraints and his mentee’s multiple interests not being satisfied by the LPM program (“Mentee has diverse interests. The time commitment was too great for her, i.e.: Termination Learning Pathway.”). Recently published informal essays by successful LPM mentees demonstrate that they are able to reflect on both the benefits of having access to individual mentoring as well as the costs, such as the time investment expected of them (Matthes et al., 2024). It may therefore be insightful to pay closer attention to nomination procedures and selection criteria and their effect on the duration or effectiveness of mentoring relationships in the LPM program.

Our conclusions are limited in the sense that we cannot ascertain whether the implemented domain activities were fitted to a mentee’s mentoring goals, motivational congruence (e.g., mastery vs. performance goals), self-regulation abilities, or pre-existing knowledge; or whether they provided adequate challenges that foster learning processes, personal growth, and intrinsic motivation. These aspects also constitute overall important motivational elements as per existing research, in the sense that they can either hinder or facilitate a continuous refinement of a mentee’s skills and overall development (C. Chen et al., 2019; Durik et al., 2006; Eccles & Wigfield, 2002; Stoeber et al., 2008; Zimmerman, 2000), and should therefore be considered in future research regarding the quality of domain activities in the LPM program.

6.3. Limitations and Future Directions

First and foremost, we would like to note that our findings are confined to the LPM program and the context in which our study was conducted. Thus, our study ought not to be considered anything but a first, curious peak into factors affecting mentee engagement in a structured, school-based talent development mentoring program, which needs to be corroborated by future research both within the context of the LPM program and within similarly structured school-based talent mentoring programs.

Our qualitative analysis is based on a small sample of written mentor reports, from which we purposely selected those reporting obstructive influences on mentoring activities within the LPM program. These results may be biased or skewed due to rater effects such as information bias and ego-involvement, where mentors’ perspective on their mentees is limited by their own interpretation and observation or where they may be hesitant to share to what degree they attribute difficulties within the mentoring process to themselves. We tried to account for some of these effects by including quantitative data from a related mentee sample in our auxiliary analyses but only focused on mentee-related factors as suggested by mentors in their reports. In order to gain a deeper understanding of processes and factors affecting mentee engagement in a school-based talent mentoring program; however, basing future research on matched mentoring pair data might be a beneficial approach. Our results provide no insight into how mentees subjectively experienced the mentoring program or the relationship with their mentor, nor into general structural or relational aspects that might influence mentoring outcomes and mentee engagement (e.g., scheduling or communication issues on the mentor’s side or mentees losing interest due to a difficult relationship with their mentor). Future research may therefore benefit from investigating, contrasting, and matching relational and structural aspects from both perspectives, such as the perceived quality of the mentoring relationship, available resources, or adherence to program recommendations (e.g., regular meetings and discussions about mutual expectations, or use of recommended material).

Although our quantitative analyses were merely intended supplementary to our qualitative findings, we want to additionally acknowledge the following limitations:

First, considering the non-negligible attrition rate in the quantitative sample, an overall caveat to our analysis is that our small sample size strongly impacts the power and subsequent generalizability of our quantitative results, as evidenced by the large confidence intervals. It is furthermore not unlikely that our mentee sample might have self-selected over time for mentoring relationships that were sufficiently resilient to, for example, ‘survive’ a prolonged contact disruption. Missing case analysis requires an ample examination of the nature of missing data and the potential influence of nested structures (Asendorpf et al., 2014; Enders, 2017; Lüdtke et al., 2017). So far, schools participating in the LPM program have informally reported many possible reasons for attrition (e.g., prolonged illness of mentee or mentor, mentors or mentees changing schools, ‘successful’ dropouts moving on to a higher stage of education, mentees undertaking an exchange year), but systematic investigations are still underway. Given, for example, that irregular mentee–mentor interaction has been found to be an indicator of ineffective mentoring relationships (Hamlin & Sage, 2011; Straus et al., 2013), it is not unlikely that interrupted or irregular mentee–mentor contact, which 11 mentor reports specifically related to the COVID-19 pandemic, eventually resulted in premature closure of the mentoring relationship and thus drop out of these mentees from our study sample. Indeed, comparisons of baseline means show that our study sample differed significantly from the dropout sample on measures of domain priority, domain interest, and domain activity. An interesting avenue for future research would therefore be to systematically analyze dropouts in student mentoring samples in order to gain insight into factors attributing to mentoring closure.

Second, regarding our measure of weekly hours spent on domain activity, we could not control for the mentee’s time invested in the talent domain pre-mentoring, as the related variables had been added at a later point in the evaluation process. It needs to be mentioned further that our domain activity scale represents overall activities (such as conducting research, reading articles, or working on projects) in terms of self-reported frequency (i.e., ‘how often’) instead of quantity (i.e., hours). The LPM program currently allows mentees to choose a domain based on existing subjects taught at their school. In our analyses, we combined mentees across all school subjects, and it cannot be ruled out that our measures of domain activity may be affected by systematic differences across domains.

The latter point also affects our measure of domain interest and domain priority. Depending on the talent domain, specialized interests and activities may form or become important at a later point, and diverse interests with little focus on a single school subject may not contradict further expertise acquisition in a specific domain, for example, psychology (Subotnik et al., 2021a). Future research may therefore want to investigate how different aspects of mentee engagement and their predictive quality as well as validity vary between domains.

Finally, we want to emphasize that the pilot phase of the LPM program was heavily impacted by the COVID-19 pandemic, as evidenced by the frequency of references in our qualitative analysis and our quantitative findings suggesting an increased contact disruption in the dropout sample. It is not unlikely that this may have affected disadvantaged students more strongly (Zierer, 2021), who may have potentially profited most from having access to school-based mentoring. Unfortunately, it is not possible to judge how our results would compare to pre- or post-pandemic times.

6.4. Conclusions

Our research gives first insights into the mentor perspective on factors impeding mentee engagement in a school-based talent mentoring program that aims to support talented students during stage 2 of their talent development trajectory.

While our combined qualitative and quantitative results suggest that reasons for reduced mentee engagement in a school-based talent mentoring program may, in fact, be very heterogeneous and require deeper investigations, we want to emphasize several key takeaways that might guide implementation of similarly structured programs:

(1)

In line with extant mentoring research in the context of academic or workplace mentoring, LPM mentors see a low commitment of mentees to activities and insufficient ability to work independently as a barrier to the successful implementation of activities and projects, impeding engagement and mentoring success.

(2)

In the context of a school-based talent mentoring program aimed at enhancing competence in a domain, LPM mentors expect mentees to sufficiently prioritize mentoring activities.

(3)

More importantly, LPM mentors specifically judge a diffusion of interests and subjective priorities in their mentees as strongly impedimental to the successful implementation of mentoring activities.

(4)

Findings of our auxiliary quantitative analysis of LPM mentee data suggest that the priority of mentoring subject may be a stronger predictor of mentee activity after two years of mentoring than overall GPA or interest in the subject domain.

Taken together, these findings emphasize the importance of ensuring a good mentee-program fit by paying specific attention to nominating those students who not only demonstrate a focused level of interest in their mentoring subject but also a high willingness to invest an adequate level of effort into their respective mentoring activities and to prioritize accordingly.

Future research should corroborate and extend our findings, both in the specific context of the LPM program and in other school-based mentoring programs focused on talent development. In particular, we encourage fellow researchers to consider factors such as nomination procedures, available resources, and mentor characteristics, such as motivation for participating in the mentoring program, implicit theories about learning and achievement, or mentoring role preferences. Moreover, further research is desirable on how mentees experience a school-based talent mentoring program and how their experiences and individual motivational–behavioral variables (e.g., relationship quality or self-regulated learning abilities) contribute to low or ineffective engagement in domain activity. It might additionally be insightful to include measures pertaining to selective motivational aspects that might affect high-ability students, such as perfectionism. Finally, we recommend a deeper exploration of external and relational factors, such as resources and support provided by schools, fidelity in terms of adherence to program guidelines, and mentor motivation, behavior, and communication.

Another promising research avenue includes additional mentee surveys to learn more about motives for participating in, and especially for, dropping out of a talent mentoring program. To date, research concerned with ineffective mentoring usually focuses on tertiary education and career coaching (Hamlin & Sage, 2011; Jeong & Park, 2020), or programs for at-risk students (Kupersmidt et al., 2017). It is not presumptuous to assume that not all resulting findings can easily be transferred to a school-based mentoring program for talent development in a subject area. Considering that talent mentoring programs may be quite resource-intensive for schools, we firmly believe that such investigations could provide information necessary for dropout prediction and potentially even dropout prevention, thus improving outcomes for mentees, mentors, and schools.