Multiple Assignment Research Articles

Implementing a Senior Statistics Practicum: Lessons and Feedback from Multiple Offerings

A Statistics Practicum course is offered as another option besides an internship or research experience for students to fulfill a required statistics major capstone experience. This article discusses the first and fourth offering of this practicum course, which provides a unique perspective on the initial implementation of the course and its development over time. The course offers students opportunities to carry out statistical consulting projects with external clients. Students were given multiple reflection assignments throughout the course. Challenges of the projects were discussed in the reflections, which included issues of data cleaning and analysis. Students also responded to both Likert-scale and open-ended questions on an end of semester survey. These responses provided information on sentiment regarding the consulting projects and perceived usefulness of various components of the Statistics Practicum course. Both student reflection assignments and survey responses were analyzed in this study. Explanations of the thought processes that went into setting up and running the course are included. Advice and suggestions for course improvements and successful administration are also presented.

RIS-Assisted Joint Transmission in a Two-Cell Downlink NOMA Cellular System

This paper investigates the integration of reconfigurable intelligent surface (RIS) with downlink non-orthogonal-multiple-access (NOMA) in a multi-user two-cell network assisted by the joint-transmission coordinated multipoint (JT-CoMP). Specifically, the RIS is deployed at the edge of two adjacent cells to assist the JT-CoMP from these two cells to multiple far NOMA users located at their edges. Under this setup, we jointly optimize the power allocation (PA) coefficients at the base stations (BSs), the user clustering (UC) policy, and the phase-shift (PS) matrix of the RIS with the objective of maximizing the network sum-rate subject to a target quality-of-service, defined in terms of the minimum required data rate at each cellular user, and the successive interference cancellation (SIC) constraints. The formulated problem ends to be a non-convex mixed-integer non-linear program that is difficult to be solved in a straightforward manner. To alleviate this issue, and with the aid of alternating optimization (AO), the original optimization problem is decomposed into two sub-problems, a joint PA and UC sub-problem and a PS sub-problem, that are solved in an alternating way. For the first sub-problem, we invoke the bi-level optimization approach to decouple the PA sub-problem from the UC sub-problem. For the PA sub-problem, closed-form expressions for the optimal PA coefficients are derived. On the other hand, the UC problem is projected to multiple 2-dimensional assignment problems, each of which is solved using the Hungarian method. Finally, the PS sub-problem is formulated as a difference-of-convex problem and an efficient solution is obtained using the successive convex approximation technique. The numerical results reveal that the network sum-rate of the proposed RIS-assisted CoMP NOMA networks outperforms the conventional CoMP NOMA scheme without the assistance of the RIS, the RIS-assisted CoMP orthogonal multiple access (OMA) scheme, and RIS-assisted NOMA scheme, especially for low transmit power from the BSs.

Accelerating Benders decomposition for stochastic incomplete multimodal hub location problem in many-to-many transportation and distribution systems

To customize the hub location model for application in many-to-many transportation and distribution systems, this study introduces a stochastic incomplete multimodal hub location problem with multiple assignments and delivery-time restrictions. This problem explicitly considers the mode-specific hub and link, incomplete inter-hub connectivity, multiple-assignment pattern of demand nodes to hubs and two types of uncertainties in an expected cost-minimization context with delivery-time restrictions. Using a filtering technique, this study first presents a sophisticated path-based formulation for the problem with uncertain demand embedded in a two-stage stochastic programming framework. More importantly, the stochastic demand model is proven to be equivalent to the corresponding deterministic expected value problem (EVP), which can be solved to optimality using Gurobi. By considering the uncertainty in transportation cost, this study further proposes an associated two-stage stochastic program, in which the EVP equivalence does not hold. To solve the stochastic transportation cost version efficiently, the study implements an improved Benders decomposition algorithm by adopting a sample average approximation approach and a dualization strategy. To accelerate the convergence of the proposed Benders decomposition algorithm, this study also presents a multi-cut reformulation and a cut-loop stabilization strategy for Benders acceleration. Numerical experiments based on the well-studied Turkish network and AP dataset corroborate the advantages of the proposed models and the effectiveness of the developed approaches. Some key managerial insights are summarized to effectively guide incomplete, multimodal hub network designs against uncertainty in many-to-many transportation and distribution practices.

Formalizing Creativity: Academic Writing in Early College Years

The transition from high school to college can easily be deemed to be one of the biggest changes in a student’s life. However, due to its dramatic nature, one may fail to fully consider the intricacies of this change. In college, the criteria for evaluating students’ academic standing shifts from simple examinations to a blend of assignments and exams. Multiple assignments need the students to write authentic pieces of academic writing. This perspective article aims at providing a conceptual description, and analysis, of academic writing during early college years. We evaluate the structure and components of various stages in academic writing. The outcomes of the paper provide a thorough analysis of dichotomies that exist in students’ experiences using two models of literacy: The Autonomous Model, and The Ideological Model. To further reemphasize how crucial early college years are, a contrast has been made between what academic writing is and should be in high school; and what academic writing is in college. This contrast is exemplified using the Central Board of Secondary Education, which is India’s primary board for education. This article also explores the academic and cognitive dimensions of academic writing. Taking the current status quo and the recent transformation of the academic world into consideration, the outcome also highlights the challenges and limitations posed by the COVID-19 pandemic and provides recommendations to tackle the same. The article also introduces and appraises the idea of students in today’s world, who majorly identify as “digital natives.” It subsequently analyses how this identity impacts their academic life in a pandemic-struck online system of education. A brief yet elaborate account of the Indian context, and its structural realities were the key focus while making inferences. Finally, the article presents limitations and challenges faced by students in three ways, reiterating the contrast between high school and college in terms of academic instruction, briefly analyzing the social environment in college along with its possible implications, and commenting upon the ambiguity in academic expectations.

“A Prescription for Play”: developing early childhood preservice teachers’ pedagogies of play

ABSTRACT Although research supports play for children’s learning and development, many teachers and schools do not support play or relinquish it to recess or “If time.” Teachers need to experience how they learn through play to advocate for play in their classrooms. It is not enough to say, “play is learning”; they need to learn through play. This study uses data from multiple classroom assignments in a course on early childhood curriculum to examine how these play-based assignments support preservice teachers’ support for play and creativity in early childhood classrooms. The study found that preservice teachers had a positive shift to supporting play and creative experiences, including an appreciation of open-ended materials. They also developed understanding of children’s perspectives, the value of play as adults and found that engaging in play and creative experiences was beneficial to preservice teachers’ mental health and self-care. These findings reveal that if we want teachers to advocate for play and creativity in their classrooms, it is essential for them to have direct experiences learning through play and creativity themselves. Recommendations suggest that teacher educators should integrate authentic play experiences for play advocacy and support preservice teachers’ mental health and well-being.

Low-Complexity Chase Decoding of Reed-Solomon Codes Using Channel Evaluation.

A novel time-varying channel adaptive low-complexity chase (LCC) algorithm with low redundancy is proposed, where only the necessary number of test vectors (TVs) are generated and key equations are calculated according to the channel evaluation to reduce the decoding complexity. The algorithm evaluates the error symbol numbers by counting the number of unreliable bits of the received code sequence and dynamically adjusts the decoding parameters, which can reduce a large number of redundant calculations in the decoding process. We provide a simplified multiplicity assignment (MA) scheme and its architecture. Moreover, a multi-functional block that can implement polynomial selection, Chien search and the Forney algorithm (PCF) is provided. On this basis, a high-efficiency LCC decoder with adaptive error-correcting capability is proposed. Compared with the state-of-the-art LCC (TV = 16) decoding, the number of TVs of our decoder was reduced by 50.4% without loss of the frame error rate (FER) performance. The hardware implementation results show that the proposed decoder achieved 81.6% reduced average latency and 150% increased throughput compared to the state-of-the-art LCC decoder.

MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions.

Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development of effective multiple-component interventions. These include factorial designs, Sequential Multiple Assignment Randomized Trials (SMARTs), and Micro-Randomized Trials (MRTs). An important challenge facing researchers concerns selecting the right type of design to match their scientific questions. Here, we propose MCMTC – a pragmatic framework that can be used to guide investigators interested in developing digital interventions in deciding which experimental approach to select. This framework includes five questions that investigators are encouraged to answer in the process of selecting the most suitable design: (1) Multiple-component intervention: Is the goal to develop an intervention that includes multiple components; (2) Component selection: Are there open scientific questions about the selection of specific components for inclusion in the intervention; (3) More than a single component: Are there open scientific questions about the inclusion of more than a single component in the intervention; (4) Timing: Are there open scientific questions about the timing of component delivery, that is when to deliver specific components; and (5) Change: Are the components in question designed to address conditions that change relatively slowly (e.g., over months or weeks) or rapidly (e.g., every day, hours, minutes). Throughout we use examples of tobacco cessation digital interventions to illustrate the process of selecting a design by answering these questions. For simplicity we focus exclusively on four experimental approaches—standard two- or multi-arm randomized trials, classic factorial designs, SMARTs, and MRTs—acknowledging that the array of possible experimental approaches for developing digital interventions is not limited to these designs.

Scheduling math practice: Students' underappreciation of spacing and interleaving.

Many randomized controlled experiments in the classroom have found that mathematics learning is improved dramatically when practice problems of one kind are distributed across multiple assignments (spaced) and mixed with other kinds of problems (interleaved). In two studies, we investigated students' knowledge of spacing and interleaving. In Study 1, 193 undergraduates designed learning schedules for a hypothetical math class. In Study 2, 175 undergraduates selected from among five hypothetical schedules in response to a variety of questions, provided reasons for their selections, and rated the utility of spacing and interleaving. In both studies, most participants incorrectly judged schedules with minimal degrees of spacing and interleaving to be most effective. Also, schedules with more spacing and interleaving were perceived as more difficult, less enjoyable, and less common. Participants' ratings of utility revealed mixed perspectives on spacing and an underappreciation of interleaving. Altogether, these findings demonstrate that most students fail to recognize the benefits of spaced and interleaved practice. Further, by identifying specific ways in which their beliefs about spacing and interleaving fall short, we reveal opportunities to reshape students' beliefs to foster these effective learning techniques. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Dimensions of skill use in the unified protocol: Exploring unique effects on anxiety and depression.

Although the Unified Protocol contains multiple distinct skills to target anxiety, depression, and related conditions, researchers have yet to establish if patients' use of these skills contributes to symptom change. Using data from the first-stage randomization of a sequential multiple assignment randomized trial, we tested whether general skillfulness, defined by skill knowledge, frequency, quality, and effectiveness, predicted within- or between-person changes in anxiety and depression. We further tested whether use of particular UP skills (e.g., mindfulness and behavior change) predicted changes in these outcomes. Participants (N = 70; Mage = 33.74, 67% female, 74% white) completed six sessions of core UP modules and reported on their skillfulness and specific skill use using two novel measures, along with their symptoms of anxiety (Overall Anxiety Severity and Impairment Scale) and depression (Overall Depression Severity and Impairment Scale) before each session, and their alliance (Working Alliance Inventory) after each session. We disaggregated between- from within-person effects to test the effects of skillfulness, skill use, and the alliance on session-to-session symptom change. Greater within-person skillfulness predicted decreases in anxiety and depression. More frequent within-person use of all UP skills predicted decreases in anxiety, whereas more frequent within-person use of alternative actions specifically predicted decreases in depression. This pattern of results provides guidance on the specific aspects of skill training to prioritize in transdiagnostic treatments depending on whether therapists are targeting anxiety or depression. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Attitudes towards and experiences with economic incentives for engagement in HIV care and treatment: Qualitative insights from a randomized trial in Kenya.

Growing literature has shown heterogenous effects of conditional cash incentives (CCIs) on HIV care retention. The field lacks insights into reasons why incentives impact various patients in different ways-differences that may be due to variations in psychological and social mechanisms of effect. A deeper understanding of patients' perceptions and experiences of CCIs for retention may help to clarify these mechanisms. We conducted a qualitative study embedded in the ADAPT-R trial (NCT#02338739), a sequential multiple assignment randomized trial (SMART) that evaluated economic incentives to support retention in HIV care among persons living with HIV (PLHIV) initiating antiretroviral therapy in Kenya. Participants who attended their scheduled clinic visits received an incentive of approximately $4 each visit. Interviews were conducted between July 2016 and June 2017 with 39 participants to explore attitudes and experiences with economic incentives conditional on care engagement. Analyses revealed that incentives helped PLHIV prioritize care-seeking by alleviating transport barriers and food insecurity: "I decided to forgo [work] and attend clinic […] the voucher relieved me". Patients who borrowed money for care-seeking reported feeling relieved from the burden of indebtedness to others: "I borrow with confidence that I will pay after my appointment." Incentives fostered their autonomy, and enabled them to support others: "I used the money to buy some clothes and Pampers for the children." Participants who were intrinsically motivated to engage in care ("my life depends on the drugs, not the incentive"), and those who mistrusted researchers, reported being less prompted by the incentive itself. For patients not already prioritizing care-seeking, incentives facilitated care engagement through alleviating transport costs, indebtedness and food insecurity, and also supported social role fulfillment. Conditional cash incentives may be an important cue to action to improve progression through the HIV treatment cascade, and contribute to better care retention.

Comparative Agent-Based Simulations on Levels of Multiplicity Using a Network Regression: A Mobile Dating Use-Case

We demonstrate the use of agent-based models to simulate the interactions of two mobile dating applications that possess divergent interaction features. We reproduce several expected outcomes when compared to extant literature. We also demonstrate the use of a standard social network analysis technique—the network regression, Multiple Regression Quadratic Assignment Procedure—in conducting a principled and interpretable comparison between the two models with strong results. This combined approach is novel and allows complex system modelers who utilize agent-based models to reduce their reliance on idealized network structures (small world, scale-free, erdos-renyi) when applying underlying network interactions to agent-based models that can often skew results and mislead from a full picture of system-level properties. This work serves as a proof-of-concept in the integration of classical social network analysis methods and contemporary agent-based modeling to compare software designs and to enhance the policy-generation process of online social networks.

Bayesian set of best dynamic treatment regimes: Construction and sample size calculation for SMARTswithbinary outcomes.

Sequential, multiple assignment, randomized trials (SMARTs) compare sequences of treatment decision rules called dynamic treatment regimes (DTRs). In particular, the Adaptive Treatment for Alcohol and Cocaine Dependence (ENGAGE) SMART aimed to determine the best DTRs for patients with a substance use disorder. While many authors have focused on a single pairwise comparison, addressing the main goal involves comparisons of >2 DTRs. For complex comparisons, there is a paucity of methods for binary outcomes. We fill this gap by extending the multiple comparisons with the best (MCB) methodology to the Bayesian binary outcome setting. The set of best is constructed based on simultaneous credible intervals. A substantial challenge for power analysis is the correlation between outcome estimators for distinct DTRs embedded in SMARTs due to overlapping subjects. We address this using Robins' G-computation formula to take a weighted average of parameter draws obtained via simulation from the parameter posteriors. We use non-informative priors and work with the exact distribution of parameters avoiding unnecessary normality assumptions and specification of the correlation matrix of DTR outcome summary statistics. We conduct simulation studies for both the construction of a set of optimal DTRs using the Bayesian MCB procedure and the sample size calculation for two common SMART designs. We illustrate our method on the ENGAGE SMART. The R package SMARTbayesR for power calculations is freely available on the Comprehensive R Archive Network (CRAN) repository. An RShiny app is available at https://wilart.shinyapps.io/shinysmartbayesr/.

A pilot sequential multiple assignment randomized trial (SMART) protocol for developing an adaptive coaching intervention around a mobile application for athletes to improve carbohydrate periodization behavior

BackgroundIt has recently been identified that manipulating carbohydrate availability around exercise activity can enhance training-induced metabolic adaptations. Despite this approach being accepted in the athletic populations, athletes do not systematically follow the guidelines. Digital environments appear to allow nutritionists to deliver this intervention at scale, reducing expensive human coaching time. Yet, digitally delivered dietary behavior change interventions for athletes and the coaching strategy to support them are still novel concepts within sports nutrition. Methods/designWe aim to recruit 900 athletes across the UK. 500 athletes will be recruited to test the feasibility of a novel menu planner mobile application with coaching for 6 weeks. 250 athletes with pre-existing nutritionist support will also be recruited as control. We will then conduct a 4-week pilot sequential multiple assignment randomized trial (SMART) with an additional 150 athletes. In the SMART, athletes will be given the application and additional coaching according to their engagement responses. The primary outcomes are the mobile application and coach uptake, retention, engagement, and success in attaining carbohydrate periodization behavior. Secondary outcomes are changes in goal, weight, carbohydrate periodization self-efficacy, and beliefs about consequences. Due to the high attrition nature of digital interventions, all quantitative analyses will be carried out based on both the intention-to-treat and per-protocol principles. DiscussionThis study will be the first to investigate improving carbohydrate periodization using a digital approach and tailored coaching strategies under this context. Foundational evidence from this study will provide insights into the feasibility of the digital approach.

Baseline data of a sequential multiple assignment randomized trial (STEP study).

Research has shown that preventative intervention in individuals at ultra-high risk of psychosis (UHR) improves symptomatic and functional outcomes. The staged treatment in early psychosis (STEP) trial aims to determine the most effective type, timing and sequence of interventions in the UHR population by sequentially studying the effectiveness of (1) support and problem solving, (2) cognitive-behavioural case management and (3) antidepressant medication with an embedded fast-fail option of (4) omega-3 fatty acids or low-dose antipsychotic medication. This paper presents the recruitment flow and baseline clinical characteristics of the sample. STEP is a sequential multiple assignment randomized trial. We present the baseline demographics, clinical characteristics and acceptability and feasibility of this treatment approach as indicated by the flow of participants from first contact up until enrolment into the trial. Recruitment took place between April 2016 and January 2019. Of 1343, help-seeking young people who were considered for participation, 402 participants were not eligible and 599 declined/disengaged, resulting in a total of 342 participants enrolled in the study. The most common reason for exclusion was an active prescription of antidepressant medication. Eighty-five percent of the enrolled sample had a non-psychotic DSM-5 diagnosis and symptomatic/functional measures showed a moderate level of clinical severity and functional impairment. The present study demonstrates the acceptability and participant's general positive appraisal of sequential treatment. It also shows, in line with other trials in UHR individuals, a significant level of psychiatric morbidity and impairment, demonstrating the clear need for care in this group and that treatment is appropriate.

Fostering cultural sensitivity amongst students of pharmacy through an interprofessional book club activity

Background and purposeTo describe a large-scale, cultural sensitivity-focused interprofessional book club activity that is required in the first-professional year of an accelerated pharmacy curriculum. Educational activity and settingAn interprofessional book club activity, focusing on the need for cultural sensitivity in health care, is conducted annually for students in the acupuncture, pharmacy, physical therapy, physician assistant, and sonography programs. Each year over 400 students are required to attend and are assigned to interprofessional groups to discuss guided questions pertaining to the book written by Anne Fadiman, The Spirit Catches You and You Fall Down: A Hmong Child, Her American Doctors, and the Collision of Two Cultures. Pharmacy students complete multiple assignments before and after the activity. Students are administered a post-survey to collect student feedback and self-assessment data. The book club has been run in both synchronous and asynchronous formats. FindingsStudent survey responses have consistently revealed that the majority of students agreed or strongly agreed that they were better prepared for culturally-diverse patient interactions, the activity allowed for interprofessional learning, and the activity should continue for future students. SummaryA book club activity is an effective strategy for delivering content related to cultural sensitivity in an interprofessional format. This activity model can be used to support interactions with multiple professions in different schools within the same university or with multiple professions located at different universities/institutions.

Science to practice: Does gamification enhance intrinsic motivation?

Self-determination theory (SDT) has empirical support in understanding and enhancing motivation in a variety of contexts, including education settings. Niemac and Ryan have highlighted that using SDT in course design can lead to stronger fulfilment of an internal locus of causality regarding course work. One course design method anchored in SDT is gameful learning—structuring tasks that support intrinsic motivation, primarily increasing autonomy over learning. A gamified classroom (GC) may offer more assignments and points than minimally necessary for students to earn a passing mark, allowing students choice in which projects to pursue. Further research is needed to examine the degree to which students’ motivations differ between a GC and a non-gamified classroom (NGC). The purpose of the current study was to determine if students in a GC were more intrinsically motivated than students in NGC. Students were enrolled in an undergraduate kinesiology course using a GC design ( n = 24) or NGC design ( n = 26) and completed an online survey – derived from the intrinsic motivation inventory and the test anxiety questionnaire—at the beginning and end of the semester. In the GC, students started with zero points, and were offered multiple assignments with scaffolded difficulty to reach their desired grade. The NGC used a traditional 100% grade range, with only required assignments and exams, and students lost points for inadequate or inaccurate responses. Following analyses, it was revealed that students in the GC had higher perceptions of autonomy and competence than students in the NGC. Where these differences exist over time, along with differences in other subscales, will be discussed further. Educators seeking to enhance student motivation and engagement may therefore look to gamification as an appropriate methodology.

Science, Poetics, and Immersive Field Ecology

Complex global challenges and declining scientific literacy demand novel approaches to engaging students with science and the natural world. While evidence supports integrating creative and scientific modes of inquiry, these approaches are often separated in undergraduate education. We designed Ecology Through the Writer’s Lens (ETWL) to allow students to explore an ecosystem of critical importance, the tall grass prairie, through an interdisciplinary field experience. Co-taught by Biology and English professors and open to students of all majors, ETWL leverages classroom activities to prepare for and process the immersive field experience over the course of one semester. Field-based exercises include natural history observations, hypothesis building, experimental design, analysis of the literature, and creative/reflective writing. Learning was assessed through multiple assignments, including a final creative project that spanned diverse writing genres. Students met and exceeded expectations with respect to course objectives. Non-science majors learned how scientific knowledge is generated; science majors learned how creative approaches can open new pathways for exploration. Many students overcame fear of natural spaces. Several students independently engaged with tall grass prairie in post-course activities. We conclude that interdisciplinary approaches to field-based inquiry can generate transformative experiences, even when the immersive component is short-term and close to home. ETWL provides one model by which different modes of inquiry can be blended to enhance student appreciation of science, literature, and the environment.

Multi-level approach to virtual commissioning: a reconfigurable assembly system case

Software plays a key role in modern automated systems. Testing the control software is one of the final steps in the development process and when done on the physical set up, it creates risks (e.g. project delay, system damage and operator injury). In addition, with flexible systems it is almost impossible to test every possible scenario on the real system. Using a digital model allows testing different configurations before the physical hardware is available and thus without risk for the operator and the system hardware. A digital model of the reconfigurable system can also help validate new configurations virtually while the real system still operates in its current configuration, allowing for a quick risk-less changeover. Depending on the level in the control hierarchy, the simulations can run faster than real time. This paper demonstrates the use of a digital model during the commissioning at different levels. On each level, other requirements need to be fulfilled and a different level of detail is needed in the model. Every level asks for a different set of input data, that needs to be consistent to result in a reliable emulation/simulation. The methodology is evaluated on a reconfigurable assembly system, consisting of multiple base units with modular, relocatable add-on modules. The most detailed level is the process and machine level, considering the low-level control logic, movements of the machine and robots, collisions, etc. Cell and plant level commissioning focuses on the interaction of multiple machines, transport, production schedule and assignment of tasks to the individual machines. This level typically covers a longer time span and requires less detail of the actions on the machine level. It interacts with higher level control software and a simplified version of the low-level control logic is implemented in the model itself. This paper describes the methodology and lessons learned to reuse the digital model at different levels of the control hierarchy while remaining the consistency by exchange of information within the digital model and with the real system. Further research is described towards automated virtual test case scenarios with a digital model for a reconfigurable assembly system.

Dynamic Multiple Multicasts Routing and Wavelength Assignment for Realizing Modified Artificial Fish Model in Mesh-Based Onoc

Dynamic Multiple Multicasts Routing and Wavelength Assignment for Realizing Modified Artificial Fish Model in Mesh-Based Onoc

Social Capital Leveraging Knowledge-Sharing Ties and Learning Performance in Higher Education: Evidence From Social Network Analysis in an Engineering Classroom

With the growing attention to the effective use of social network analysis in explaining student learning in STEM (science, technology, engineering, and mathematics) education, the authors explore why college students share knowledge and how they achieve their learning in a laboratory class. In particular, the authors investigate how the social capital each student builds influences individual knowledge sharing and its related learning. The authors establish a research model derived from social capital theory that explains the relational, structural, and cognitive capital the class builds. Network data from 120 students in a lab class in a mechanical engineering department in the United States were collected and analyzed to test the research model and hypotheses using the multiple regression quadratic assignment procedure and UCINET. The findings show that the knowledge sharing mechanism and student learning outcomes can be explained in terms of their social capital within social networks. Last, the authors discuss the theoretical and practical implications for college student learning in a laboratory class in STEM education.