Improving STEM Education in Research: Preliminary Report on the Development of a Computer-Assisted Student-Mentor Research Community

doi:10.4236/ce.2012.35090

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Creative Education

2012. Vol.3, No.5, 612-618

Published Online September 2012 in SciRes (http://www.SciRP.org/journal/ce) http://dx.doi.org/10.4236/ce.2012.35090

Improving STEM Education in Research: Preliminary Report

on the Development of a Computer-Assisted Student-Mentor

Research Community

David Rios1, Artem Chebotko1, Christine Reilly1, Ralph Carlson2, Emmett Tomai1,

Amy A. Weimer3, Nicholas Weim er4, Thomas Pearson5, Francis Andoh-Baidoo6,

Robert Wi nkl e7,8, David Ammons9, Joanne Rampersad9*

1Department of Computer Science, The University of Texas-Pan American, Edinburg, USA

2Department of Educational P sychology, The U n i versity of Texas-Pan American, Edinburg, USA

3Department of Psychology, The University of Texas-Pan American, Edinburg, USA

4College of Social and Behavioral Sciences, The University of Texas-Pan Amer ic an , Edinburg, USA

5Department of Histor y and Philosophy, The University of Texas-Pan American, Edinburg, USA

6Department of Computer Information Systems and Quantitative Methods,

The University of Texas- Pa n American, Edinburg, USA

7Department of Political Science, The University of Texas-Pan American, Edinburg, USA

8Center of Excellence in STEM Education, The University of Texas-Pan American, Edinburg, USA

9Department of Chemistry, The University of Te x as -Pan American, Edinburg, USA

Email: *[email protected]

Received July 3rd, 2012; revised August 7th, 2012; accepted August 1 9th, 2012

Research education in STEM disciplines currently suffers from 1) The inability to feasibly collect highly

detailed data on both the student’s and mentor’s activities; 2) The lack of tools to assist students and men-

tors in organizing and managing their research activities and environments; and 3) The inability to corre-

late a student’s assessment results with their actual research activities. Together these three problems act

to impede both the improvement and educational quality of student research experiences. We propose a

computer-assisted student-mentor research community as a solution to these problems. Within this com-

munity setting, students and their mentors are provided tools to make their work easier, much like a word

processor makes writing a letter easier. Through their use of these tools, details of student-mentor activi-

ties are automatically recorded in a relational database, without burdening users with the responsibility of

archiving data. Equally important, student assessments of outcome can be directly related to student activ-

ity, allowing educators to identify practices resulting in successful research experiences. Community tools

also facilitate the use of labor-intensive teaching laboratories involving real inquiry-based research. The

community structure has the added benefit of allowing students to see, communicate and interact more

freely with other students and their projects, thus enriching the student’s research experience. We provide

herein a preliminary report on the development and testing of a prototype, student-mentor research com-

munity, and present its tools, an assessment of student interest in participating in the community, and dis-

cuss its further development into a nationally-available student-mentor research community.

Keywords: Cooperative/Collaborative Learning; Architectures for Educational Technology System;

Computer-Mediated Communication; Evaluation Methodologies; Learning Communities

Introduction

Providing students with research experiences in the disci-

plines of Science, Technology, Engineering and Mathematics

(STEM) is a nationally recognized objective (STEM Education

Coalition; US Department of Education; National Science B oa r d) .

However, despite considerable expenditures of private and public

funds to provide research experiences (The National Conferences

on Undergraduate Research; National listing of REU Programs),

it is surprising that success is still being measured by the num-

ber of students participating, money spent, and largely anecdo-

tal evidence of whether the research experience was enjoyable

(see e.g., Crowe et al., 2008). To address this problem, some

programs offering research experiences have turned to the sci-

entific assessment of students’ experiences/activities (Russell,

2006; Lopatto: Surveys of undergraduate experien ces). Although

laudable, such assessments are of limited utility because they

cannot presently be linked to specific research activities, mak-

ing it difficult to correlate outcome with students’ actual activi-

ties. Equally unde sirable is that accountability is diminished wh en

a student’s research activities are not known, since it cannot be

demonstrated whether students participated in true research (for-

mulated hypothesis, designed experiments, etc.), nor whether

they were actually “mentored” by their mentor. To ensure both

accountability and the quality of research experien ces, a detail ed

understanding of student activities and interactions with their

mentors must be both known and associated with student as-

sessment. However, it simply is not feasible to expect students

and mentors to make note of every meeting, updated research

*Corresponding author.

D. RIOS ET AL.

goal, time spent in the laboratory, fellow students that they men-

tored, presentations given, etc.—it is simply too cumbersome,

and history has shown that this approach does not work. Un-

fortunately, many of these problems are not adequately ad-

dressed by different, existing mentoring software (see for ex-

ample, Chronus, Icouldbe and iMentor).

A possible solution to the problem is found in a community

structure. The benefit of a structured community is that it pro-

vides tools and services that make activities easier, while auto-

matically providing detailed information about a member’s ac-

tivities and relationships. For example, the act of using a credit

card (a tool), intended or not, documents our personal prefer-

ences, interests, physical location, buying habits, psychological

parameters (e.g., willingness to incur debt, and the level and

type of debt we are willing to incur), etc. This raises the ques-

tion of whether a stude nt-mentor community, that provides tool s

facilitating student-mentor research activities, can automatically

document the details of a student’s research activities. If so,

then large amounts of detailed information on student/mentor

activities could be collected. The challenge is to design a com-

munity with computer-assisted tools that members find useful

and want to use, but that are also capable of automatically col-

lecting the desired information. Herein we provide a prelimi-

nary report on the development, testing and students’ percep-

tion of prototype software that supports a community of stu-

dent-mentor researchers.

Materials and Methods

Developing Prototype Community-Building Software

Prototype community-building software called Student Re-

search Organizer (SRO) was developed to create a local student

research community on the campus of the University of Texas-

Pan American (UTPA). UTPA Institutional Review Board ap-

proval was obtained to develop and implement the prototype

software. Informed conse nt from participants was obtained. SR O

used a server-mounted MySQL database with a user interface

built with Microsoft Access. SRO was designed from the bot-

tom up (i.e., functionality was primarily added and perfected by

satisfying needs and addressing comments from students and

their Mentor, as opposed to building the community based on a

preconceived design). SRO thus went through conti nuous growth,

evaluation, and improvement cycles. To promote its widespread

use, SRO development was guided by a user-oriented philoso-

phy based on two key functional objectives: 1) To provide tools

to facilitate activities that students and mentors are normally

engaged in (i.e., to make their existing activities easier/more

productive for them to perform); and 2) Not to burden users by

requesting information that does not pertain to their activities

(i.e., minimize the collection of information solely for use by

education researchers or administrators). SRO was initially ac-

cessible to students only from computers in a single laboratory,

which eventually was upgraded to any computer on the UTPA

campus 24/7, including students’ own personal computers.

Research Environment

All activities described in this report occurred at the UTPA, a

predominantly Hispan ic-ser ving institut ion located along th e Texas

border with Mexico that is recognized as a Predominately Un-

dergraduate Institution (PUI). A large portion of the university’s

students come from low income families with almost 80% of

the students receiving need-based grants or scholarships. UTPA

receives substantial funding from both public and private or-

ganizations to facilitate the participation of students in STEM-

related research. SRO was used in both a guided/open inquiry

research teaching laboratory (defined according to Buck et al.,

2008; Advanced Biochemistry Laboratory 3rd year course, Spring

2009, 2010, 2011), and continually from 2008-present with stu-

dents participating in research in a Faculty laboratory. In both

cases the mentor was study participant Dr. Joanne Rampersad.

Student Perceptions of SRO

Subjects

Forty-four subjects responded to 15 items on the Student In-

terest in Using-SRO scale (SIU-SRO) after using SRO in a sci-

ence laboratory. Forty one subjects, consisting of two groups,

twenty-one males (n = 21), and twenty females (n = 20) com-

pleted the (SIU-SRO) scale.

Measures

A scale for measuring SIU-SRO was administered to sub-

jects after utilizing SRO. The 15 items on the SIU-SRO used an

equal appearing eight point Likert Scale.

Exploratory factor analysis was used to derive underlying

dimensions that the SIU-SRO was measuring. Varimax rota-

tions with an eigenvalue set at 1.0 was used to derive the un-

derlying dimensions. Two underlying domains were obtained

with the first factor explaining 54.36% of the SIU-SRO vari-

ance, and the second factor explaining 23.60% of the SIU-SRO

variance. A total of 77.97% of the total SIU-SRO variance was

explained by these two factors. The first factor was measuring a

positive attitude toward using SRO, and the second factor was

measuring a negative attitude toward using SRO.

Raw scores from the two factors were transformed into two

scales, Positive Interest in Using-SRO (PIU-SRO) and Nega-

tive Interest in Using-SRO (NIU-SRO) through a linear trans-

formation and thus deriving a common metric for the two scales.

Ten items loaded on the first factor, PIU-SRO. These load-

ings ranged from .68 to .94. Four items loaded on the second

factor, NIU-SRO. These loadings ranged from .67 to .97. Item

number 14 was deleted because of its cross loading of .60

and .64 on factors I and II, respectively. The Cronbach’s alpha

reliability coefficients for the PIU-SRO and the NIU-SRO

were .97 and .89, respectively. Given that only 41 subjects were

used in deriving the psychometric properties of the two scales,

caution should be maintained in interpretation of factor struc-

ture and pattern. A simple structure was obtained, however, and

the Cronbach’s alpha reliability coefficients of .97 and .89 for

the two scales indicate that these underlying dimensions can be

assumed to represent the phenomena of a PIU-SRO and NIU-

SRO. In addition, the discrimination indices for items on the

PIU-SRO ranged between .74 and .96, and items on the NIU-

SRO ranged between .77 and .93.

Creating a Da ta base Schema for a Nati o nal

Web-Based Community

Efficient and scalable data management is an important re-

quirement for the SRO system. Once the prototype SRO soft-

ware had been built, developed and tested, a better understand-

ing of what data should be collected and stored in the SRO dat a-

base emerged. The design of the database for the web-based

community was then carried out using a three-step methodology

commonly employed for designing relational databases. First, a

D. RIOS ET AL.

614

conceptual data model was designed using the entity-relation-

ship modeling methodology (Chen, 1976), as shown in Online

Resource 1. Second, the entity-relationship model was trans-

lated to a relational data model (Codd, 1970). Finally, the rela-

tional data model was used to obtain a database schema with

physical tables, data integrity constraints, indices and triggers.

The database schema was expressed using Structured Query

Language and instantiated in MySQL (MySQL).

dents nowadays gravitate toward social community environ-

ments (e.g., the success of community-oriented resources such

as Facebook, and Twitter attest to this); 2) A community struc-

ture allows students to see, communicate and interact more freely

with other students and their projects, thus enriching the stu-

dent’s research experience; 3) An organized, structured com-

munity of student researchers and their mentors facilitates edu-

cators in conducting educational research, quickly/broadly im-

plementing new educational methods, and allows assessment

results to be linked with actual activities; 4) A student-mentor

research community can provide needed accountability of the

Results and Discussion

The Advantages of Choosing a Community Structure educational process by documenting activities in context to spe-

cific relationships; and 5) A student-mentor community can be

used to help teach students ethics and behavioral norms that

Intuitively there are many reasons to look to a community-

based structure to improve student research education: 1) Stu-

Online Resource 1.

Entity-relationship diagram representing the conceptual data model of the SRO system.

D. RIOS ET AL.

will facilitate the development of their professional identities.

Technology can play an important role in managing educational

activities, such as a community of researchers (Sharaf & Mu s a wi ,

2011).

Structural Design of the SRO Student-Mentor

Community and Its Approach to Training Students in

the Responsible Conduct of Re se ar ch

SRO establishes a community structure based on the concept

of a population of student researchers that engage in activities

with mentors. The SRO model allows an individual to become

a community member only once, but a member can have a lim-

itless number of “roles”, where each role can establish multiple

relationships with one or more other roles in the community.

Student roles are based on the university or institution they

belong to, while mentor roles are defined by the university

department, or non-university institution (e.g., name of high

school), they belong to. Once a relationship is formed between

a student and mentor, all activities that arise from the relation-

ship are automatically documented and attributed to the two

roles that define the relationship. Thus a member of the SRO

community will always have just 1 username/password to enter

the community, at which point they will choose which specific

role and associated relationship they want to assume. Figure 1

presents a screen shot of a supervisor’s account on the proto-

type SRO software, highlighting a student’s “Projects and

Goals” page, and the user interface design.

SRO’s structural design also addresses the common privacy

issues that arise in a community setting, as well as providing a

framework to help train students in the Responsible Conduct of

Research (RCR). SRO was premised on character-building

models, as opposed to rule-governed models of ethics education,

which are inadequate in several respects. Rules are invariably

formulated in the most generic terms to be applicable to a vari-

ety of cases; but that very generality renders their application to

specific situations uncertain. The complexity of many ethically

congested cases in research makes it difficult to determine re-

liably when a situation properly fits under a particular rule. In

addition, “the rules run out,” meaning that new circumstances

give rise to novel cases that have not previously been addressed

by any rule. This is notably common in scientific research, where

new techniques and discoveries often provoke unexpected sit ua-

tions that demand a moral judgment from the community of

researchers, but for which no articulated rule has yet emerged.

Finally, rule-governed models in ethics tend to treat moral de-

cision-making as an exercise in problem-solving, as if moral

issues were a kind of puzzle in search of a satisfying resolution.

This portrays ethical concerns in an atomistic fashion, as dis-

crete dilemmas, and emphasizes the perfection of abstract tech-

niques of calculation as the proper method for seeking a solu-

tion to these dilemmas. Not only does this produce a failure to

recognize the inherent connections between many different ki nd s

of ethical situations, but it removes the human element from

moral deliberation: on a rule-governed model, a computer could

as easily do the calculations leading to a good moral decision as

Figure 1.

A screenshot, from a supervisor’s account, of the prototype SRO’s main user interface window opened to the

“Projects and Goals” tool.

D. RIOS ET AL.

could any human being—and perhaps with even greater effi-

ciency and accur acy.

By cont rast, character-building models do not focus on rule-

governed strategies for moral decision-making, but rather on

crafting the conditions under which individuals can develop a

self-understanding informed by virtue that frames their con-

scious identity as researchers. Instead of asking the question,

“What decision should I make?” as rule-governed systems do,

character-building models ask the question, “What kind of per-

son do I want to become as a researcher, such that I can make

better decisions?” These latter models emphasize the intentional

maturing of moral expertise through immersion in the practices

of scientific research, learning to appreciate and absorb the st an-

dards of ethical excellence embodied in the activities of resea rc h ,

and responding to the example and counsel of exp erienced men -

tors within the community of scientific researchers. While af-

firming the usefulness of rule-governed models for exercises in

moral decision-making, we argue that the nurturing of moral

character is a necessary preparation for such decision-making,

and thus is an endeavor that properly comes before making

moral decisions. In short, a person already possessed of a good

moral character is more likely to make appropriate ethical deci-

sions. We believe the character-building approach promises to

enhance the moral acuity of student researchers as they engage

the issues related to RCR.

The Advantages of Joining the SRO Research

Community

The success of any community depends on what the commu-

nity offers its members. If joining the SRO community did not

represent a clear benefit to the mentor or student, they would

either not join or not fully participate. It is for these reasons that

SRO was built on the philosophy of providing tools and activi-

ties that members want, and benefit from (and thus be most

likely to use), and where information important to students, men-

tors, educators and administrators can be collected indirectly

and automatically as individuals use the community’s tools to

facilitate their daily research-related work and activities.

To ensure the inclusion of tools and functions that the user

would find useful, the community prototype (SRO) was built

from the bottom-up, starting out as nothing more than a plat-

form to assign a student a research project, and set/update the

student’s research goals over time. From this very fundamental

student-mentor interaction, functions were added in response to

suggestions made by students and the mentor. For example, the

mentor in this study had approximately 15 undergraduate re-

search students working in her laboratory who would randomly

pop into her office to discuss their work. The resulting chaos

created severe problems for the Mentor. In response, a commu-

nity tool was added that allowed students to request a meeting,

which, if approved by the mentor, was added to the Mentor’s

meeting schedule, and made visible to all students. Similarly,

students found that they needed to contact other students in

their research group or laboratory, but keeping updated lists of

student emails was a problem. From this need, a community

mail system was added that allowed students to send mail based

on community criteria, such as students participating on a par-

ticular research project. Developed in response to a need ex-

pressed by either a student or their mentor, some of the other

principle user functions included in the prototype SRO com-

munity were: defining/assigning research projects to students,

updating research goals, organizing student-to-student skill

training, managing and reserving research equipment, manag-

ing drafts and deadlines for posters and presentations, organiz-

ing/documenting students’ laboratory safety training, organiz-

ing collaborators and student scholarships, an advisor window

to automatically alert students and mentors to any community

activity that requires their attention, criteria-driven reports for

obtaining information such as a list of students possessing a

particular laboratory skill, and deploying student assessments

and collecting responses.

Students Reported Benefits from Participating in the

Student-Mentor Community.

Analysis of Results

The Positive Interest in Using-SRO (PIU-SRO) scale and the

Negative Interest in Using-SRO (NIU-SRO) scale were admin-

istered to 41 students who used SRO in the research and teach-

ing labs. There were 21 male and 20 female students included

in the study. Obtained descriptive statistics are shown in Table

1 below.

A two-way factorial ANOVA (2 × 2) with one between sub-

jects factor, gender, and one within subjects factor, scales/trials,

was used to analyze obtained data (see Table 2).

Summary and Interpretation of Results

There was a difference between PIU-SRO, mean of 4.38 and

NIU-SRO, and mean of 2.02, F = 186.9 (1, 39), p < .05. The

effect size for this obtained difference is assessed through a

partial eta squared of .83 (see Table 2) and Cohen’s d value of

1.99 or approximately two unit size difference between PIU-

SRO and NIU-SRO in favor of a PIU-SRO. There is no differ-

ence between means for males and females (see Table 2). The re

is a difference between the average PIU-SRO compared to the

average NIU-SRO. From this we can conclude that after using

SRO, students have significantly greater interest/motivation in

using SRO than a disinterest.

Table 1.

Means and standard deviations for the PIU-SRO and NIU-SRO for mal e s

and females.

Positive Interest in

Using SRO Negative Interest in

Using SRO

Groups N mean SD mean SD

Males 21 4.20 .73 2.27 .63

Females 20 4.57 .53 1.75 .53

Both Sexes41 4.38 .66 2.02 .63

Table 2.

Two-way (2 × 2) factorial ANOVA for groups and scale/trials.

Source of variationSS dF MS F Partial eta 2

Between subj ects5.24 40

Between groups.13 1 .12 .93 .02

Error (b) 5.12 39 .13

Within subjects 143.9941

Scales 115.681 115.68 186.9* .83

Gender X Scales4.09 1 4.09 6.60* .14

“Error” (w) 24.2239 .62

Total 149.2381

*p < .05.

616

D. RIOS ET AL.

Perhaps the biggest surprise encountered while working with

the community prototype was the student’s unexpected interest

in documenting their effort. Students participating in research

experiences will commonly work long hours and, at times,

engage in activities that do not directly pertain to their research,

such as instructing other st udents on performing laboratory te ch-

niques. Normally, all this work and effort is not reflected in the

student’s research output, nor recognized by their mentor and

others. Unexpectedly, we found that students appreciated that

by conducting their research activities within the SRO commu-

nity, these efforts were both documented and made available to

their mentor. For example, a “Time Card” was added for stu-

dents to clock in and out of the laboratory. Although useful to

the mentor in managing the laboratory and monitoring student

effort, it was initially feared that students would find this tool a

burden, intrusive or even accusatory-surprisingly, the opposite

was true. Students were actually very upset when, due to a

downed server, they could not record their time spent working

in the laboratory. Students would even contact the Commu-

nity’s Administrator and demand that they be credited unre-

corded time. The student’s high level of interest in documenting

their effort has influenced greatly the design of student-oriented

tools to document student effort. Based on this finding, func-

tions that give students a community rating are also being de-

veloped. For example, a student that is in the top 5% for help-

ing other students with skill learning will be recognized on the

community (i.e., a special community title, etc.)

Creating a Data Management Model for a National

Web-Based Community

From the information technology and computer science per-

spective, SRO is a complex computer-based information sys-

tem that involves human and computational resources to gather,

process, analyze, and preserve data. The importance of a well-

defined data model for SRO ca nnot be underestimated. T hrough

the course of several years, as the prototype software was de-

veloped and tested in the production environment, an ad-hoc

approach to data modeling and database design “on-demand”

was in use. The experience and insights gained from this proc-

ess enabled us to take SRO’s data model to the next level using

a three-step methodology commonly employed for designing

relational databases.

The conceptual data model serves as the first and most com-

plex step in the database design process. Data collection and

management in SRO relies on the extensible conceptual data

model designed using the entity -relationship methodology (Che n

1976), the diagram of which is presented in Online Resource 1.

Entity types (rectangles) and relationship types (diamonds) in

the diagram are organized into 17 modules (shaded boxes) that

support different functional requirements of the SRO system,

including the recording of information about community mem-

bers, their roles, education, employment, research activities,

projects, certifications, scholarships, grants, assessments, pres-

entations, and so forth. As the system evolves, new modules

can be added or existing ones can be extended to address new

requirements.

The design of the relational model (Codd, 1970) is the next

step in the design process. Using standard procedures, the SRO

entity-relationship model is translated into a relational model

(also referred to as a logical data model) resulting in approxi-

mately 100 relations with various integrity constraints (not sh own

in this work). These integrity constraints ensure that the data

stored in the database will always be in a consistent state that

reflects the real community.

The third step in the design process is creation of a physical

data model based on the relational data model obtained in the

previous step. The physical data model is represented by a set

of statements written in Structured Query Language, which can

be executed to create a database schema in a Relational Data-

base Manageme nt Sy stem (RDBMS ), such a s MySQL ( MySQ L ).

In addition to the tables that store the data, the resulting data-

base schema includes a number of indices that can support effi-

cient querying of the database and multiple triggers, which are

automatic procedures that maintain data integrity.

We expect that as the SRO community grows, both mentors

and students will need SRO to provide the functionality neces-

sary to track collaboration data not contemplated in our current

design. Perhaps the greatest benefit achieved through our data-

base design process was the creation of an open-ended model

that supports both the growth of the community and the future

addition of new community roles and student activities.

We are currently in the process of building a web-interface to

interact with the database that replicates the main functions,

tools and architecture developed in the SRO prototype, along

with new capabilities. This web-interface will allow community

members to easily interact with SRO without requiring them to

have knowledge of the underlying data model.

New SRO Capabilities

While developing the SRO prototype, due to the high level of

effort/resources required, it was not feasible to develop the

temporary prototype software to support and test three commu-

nity needs. However, these needs are being addressed in the

national web-based community currently under development,

and are discussed below.

“Kids” Are Also Members of the Community

The SRO prototype community was primarily designed for

undergraduate and graduate students and their mentors. How-

ever, there is a very large body of younger students that par-

ticipate in research via this nation’s Science Fair program. Al-

though the student-mentor structure of the SRO research com-

munity would work well for student researchers participating in

the Science Fair, the community could be improved by adding

tools specifically for their unique needs. Unlike university stu-

dents, science fair students are much less experienced and ra rely

have a dedicated full-time mentor to assist them. We are there-

fore interested in taking advantage of the SRO community s truc -

ture by facilitating student-student mentorship, where college

students can assume the community role of a mentor for Sci-

ence Fair students, thereby promoting the participation of uni-

versities in the national Science Fair system. We would also

like to provide Science Fair students with special tools that will

help them develop their projects in compliance with the Scien-

tific Method. Unquestionably, helping to motivate and educate

these younger researchers is an exciting challenge in which the

SRO community can play a significant role.

Communities Benefit from Experienced Elders

As a student begins her/his research, perhaps through par-

ticipation in the Science Fair, followed by undergraduate and

post graduate studies, the student gains a wealth of experience,

D. RIOS ET AL.

618

not only in performing research but in knowing what it is like

as a student to face the challenges of research. Unfortunately,

unless the student decides to stay in both teaching and research,

all this experience and knowledge will be lost from the com-

munity. Individuals who have gone on to positions that do not

include mentoring should be enticed to stay active within the

community and act as mentors to science fair students, or per-

haps, through a forum, provide advice to students facing the

same challenges they did. Retaining the experience of commu-

nity members is important to the success of the research com-

munity, and is being actively pursued.

Good Researchers Do Not Always Make Good Mentors

It is difficult at times for a mentor to realize that a student is

having trouble, and even more difficult to know what to do to

address the problem. This is especially true for mentors with

relatively large numbers of students. The SRO student-mentor

community would therefore benefit from tools that automati-

cally identify problems with students and provide suggestions

on how to deal with the problem. The SRO community struc-

ture, and the large amount of detailed information it collects, is

ideally suited for the task. The large and diverse amount of data

on time spent in the laboratory, assessment results, student and

mentor evaluations of the student’s performance during each

goal cycle, etc. can all be used to identify potential problems

with a student. For example, SRO might, based on certain indi-

cators, notice that a student is experiencing a possible loss in

self-confidence. SRO could then automatically alert the mentor

to the potential problem, display a summary of the indicators

that reflect the problem, and then offer the mentor suggestions,

such as reducing the number and complexity of the research

goals/shortening the goal cycle to help boost the student’s labo-

ratory success and confidence.

Conclusion

Prototype software was developed that successfully support

undergraduate student-mentor activities through a community

structure. By using community tools to facilitate their work,

details of the students’ activities were automatically gathered in

a relational database without negatively burdening the students.

In fact, after using SRO, students showed a significantly greater

interest/motivation in using SRO than a disinterest. Student

assessments were easily deployed via the SRO software and the

results associated with the student’s research activities, thus

allowing a direct correlation between assessment responses and

the activities being assessed. An Entity Relationship model based

on the prototype software was created, from which a database

schema was obtained for the establishment of a larger nation-

wide studen t-m en tor res ear ch com m unit y. T hree co mm unit y needs

not incorporated in this study’s prototype software were identi-

fied for inclusion in the nation-wide community.

Acknowledgements

We would like to thank Dr. Wendy Fowler and Mr. Robert

Jackson for providing computing resources and technical assis-

tance.

This material is based upon work supported by, or in part by,

the US Army Research Laboratory and the US Army Research

Office under grant number W911NF-11-1-0150.

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