Advances in molecular biology have led to some surprising discoveries. One of these includes the complexities of RNA and its role in gene expression. One particular class of RNA called microRNA (miRNA) is the focus of this paper. We will first briefly look at some of the characteristics and biogenesis of miRNA in plant systems. The remainder of the paper will go into details of three different approaches used to identify and study miRNA. These include two reverse genetics approaches: computation (bioinformatics) and experimental, and one rare forward genetics approach. We also will summarize how to measure and quantify miRNAs, and how to detect their possible targets in plants. Strengths and weaknesses of each methodological approach are discussed.

Web 2.0 technology is expanding rapidly from social and gaming uses into the educational applications. Specifically, the multi‐user virtual environment (MUVE), such as SecondLife, allows educators to fill the gap of first‐hand experience by creating simulated realistic evolving problems/games. In a pilot study, a team of educators at the University of Nebraska‐Lincoln and eXtension created a soil and water environmental case study using SecondLife in eXtension's Morrill2 Island for testing and use by students in an introductory soil science course (n = 126). In this pilot test with a class period of 110 minutes, approximately half of the students (n = 64) were first given an orientation on how to navigate in SecondLife before beginning the soil and water pollution activity. Another group of students (n = 62) formed a control group and completed the same activity using a traditional paper and pencil method and using supporting data presented in table or graphics format. A pre‐activity survey suggested that about 33% of all students had some level of experience with virtual environments and/or playing computer/video games. Results from a randomized experiment showed that the average post‐test score for the control group was 8.38 (out of a possible 12 points), which was significantly higher than the 7.34 for the SecondLife group. Post‐activity student survey results suggested that students prefer to have educationally designed virtual interactive objects such as simulation activities and experiments, characters with whom to interact and gain information, and overall more action and gaming features to benefit their educational experience. While SecondLife and other simulation software packages have potential for educational use, in order to improve learning, the design of the activity within the technology must be pedagogically sound and also create tasks that capture and engage the learner.

In plant breeding and genetics research, plant breeders establish a hypothesis to explain how they think a particular trait is inherited, such as if it is due to one gene with complete dominance, an interaction of more than one gene, or quantitative inheritance, with many genes contributing, etc. Next the breeder sets up some crosses and observes the resulting progeny to test that inheritance hypothesis. However, when the data is collected, oftentimes the breeder discovers the number of plants observed in each class is not exactly what was expected from the hypothesis. The question then is how do plant breeders determine if the data supports their hypothesis or not? Following a tomato disease resistance example in this lesson, you will learn a simple statistical test that breeders can use to conclude if the experimental data supports their hypothesis. This lesson is written for undergraduate and graduate students studying plant breeding, as well as agriculture professionals unfamiliar with the use of the chi‐square analysis. After completing this lesson module you should be able to: Calculate expected phenotypic and genotypic ratios and the number of plants expected in each class for a given plant breeding scheme. Calculate chi‐square values for plant genetics data sets from both phenotypic and genotypic observations. Calculate degrees of freedom. Accurately interpret results from a chi‐square test. Identify appropriate uses and limitations of the chi‐square test in plant breeding and genetics research.

Learning objects originally developed for use in online learning environments can also be used to enhance face-to-face instruction. This study examined the learning impacts of online learning objects packaged into modules and used in different contexts for undergraduate education offered on campus at three institutions. A multi-case study approach was used, examining learning impacts across a variety of course subjects, course levels (introductory and advanced undergraduate), student levels (undergraduate and graduate), and instructional goals (i.e., replacement for lecture, remediation). A repeated measures design was used, with learning data collected prior to viewing the online module, after completion of the module, and at the end of the semester. The study provided a broad examination of ways that online modules are typically used in a college classroom, as well as measured learning effectiveness based on different instructional purpose and usage contexts. Results showed the effectiveness of the modules in serving as a substitute for classroom lecture, remediation of course prerequisite material, introduction to content with follow-up lab practice, and review for final exams. In each of these cases, the use of the modules resulted in significant learning increases, as well as retention of the learning until the end of the semester.

Students who feel like part of a classroom community gain more enjoyment and are more academically successful than students who do not feel similar levels of community. This study intended to determine if students in online courses perceive the same level of community as students in face‐to‐face classes and if outside factors impacted community perceptions. The Classroom Community Survey (CCS) was administered to students in three introductory‐level science classes, each with a face‐to‐face section and an online section. The CCS consists of 20 questions, measuring overall community and two subscales, connectedness and learning. Five possible responses were given scores of 1 through 5 for a total of 100 possible points. Demographic questions were asked to establish if out‐of‐class factors affected community scores. Students in face‐to‐face sections (n = 183, M = 58.10) had significantly higher community scores than online students (n = 74, M = 55.24), t (255) = 3.55, p < 0.05. Connectedness scores for students in face‐to‐face sections were significantly higher than scores for their online counterparts, t (255) = 2.81, p < 0.05. Scores for the learning subscale were not significantly different based on course delivery method, t (255) = –1.80, ns. Of the eight demographic questions, only the question regarding if the course was required had a significant impact on community scores, t (186) = 2.95, p < 0.05. Results of this study showed that face‐to‐face students perceived significantly higher levels of community than did online students. Perception of learning and course grades were not significantly different for students across delivery methods.

Plant genetic resource collections are national treasures that are critical to the success of breeding programs and the long‐term resiliency of agriculture in the United States and worldwide. The USDA National Plant Germplasm System (NPGS) is a coordinated network of 19 genebank locations throughout the United States that conserve and protect nearly 600,000 accessions representing ∼16,000 plant species. The expertise of current curators must be captured, and training materials must be developed to educate the next generation of those who maintain and use plant genetic resources. A group of experts convened in April 2018 to discuss the needs, pedagogical approaches, educational content, delivery platforms, and mechanisms for sustaining a possible future plant genetic resources management training program. A three‐component approach was envisioned to achieve the task of educating current and future genebank managers, as well as those who use genetic resources in their research and breeding programs. The proposed training program will include the development of online Resource Libraries, online courses, and workshops. Resource Libraries, hosted by GRIN‐Global, will make learning objects, downloadable information, and links to other online sources publicly available. These Resource Libraries will be available for use in existing classes, as well as for the development of new workshops and online courses. Development of, and public access to, training resources will capture key information about genebanking, make it more widely available, and secure its long‐term viability.

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Aim/Purpose: This study examines differences in credit and noncredit users’ learning and usage of the Plant Sciences E-Library (PASSEL, http://passel.unl.edu), a large international, open-source multidisciplinary learning object repository. Background: Advances in online education are helping educators to meet the needs of formal academic credit students, as well as informal noncredit learners. Since online learning attracts learners with a wide variety of backgrounds and intentions, it is important understand learner behavior so that instructional resources can be designed to meet the diversity of learner motivations and needs. Methodology: This research uses both descriptive statistics and cluster analysis. The descriptive statistics address the research question of how credit learners differ from noncredit learners in using an international e-library of learning objects. Cluster analysis identifies high and low credit/noncredit students based on their quiz scores and follow-up descriptive statistics to (a) differentiate their usage patterns and (b) help describe possible learning approaches (deep, surface, and strategic). Contribution: This research is unique in its use of objective, web-tracking data and its novel use of clustering and descriptive analytic approaches to compare credit and noncredit learners’ online behavior of the same educational materials. It is also one of the first to begin to identify learning approaches of the noncredit learner. Findings: Results showed that credit users scored higher on quizzes and spent more time on the online quizzes and lessons than did noncredit learners, suggesting their academic orientation. Similarly, high credit scorers spent more time on individual lessons and quizzes than did the low scorers. The most striking difference among noncredit learners was in session times, with the low scorers spending more time in a session, suggesting more browsing behavior. Results were used to develop learner profiles for the four groups (high/low quiz scorers x credit/noncredit). Recommendations for Practitioners: These results provide preliminary insight for instructors or instructional designers. For example, low scoring credit students are spending a reasonable amount of time on a lesson but still score low on the quiz. Results suggest that they may need more online scaffolding or auto-generated guidance, such as the availability of relevant animations or the need to review certain parts of a lesson based on questions missed. Recommendation for Researchers: The study showed the value of objective, web-tracking data and novel use of clustering and descriptive analytic approaches to compare different types of learners. One conclusion of the study was that this web-tracking data be combined with student self-report data to provide more validation of results. Another conclusion was that demographic data from noncredit learners could be instrumental in further refining learning approaches for noncredit learners. Impact on Society: Learning object repositories, online courses, blended courses, and MOOCs often provide learners the option of moving freely among educational content, choosing not only topics of interest but also formats of material they feel will advance their learning. Since online learning is becoming more prolific and attracts learners with a wide variety of backgrounds and intentions, these results show the importance of understanding learner behavior so that e-learning instructional resources can be designed to meet the diversity of learner motivations and needs. Future Research: Future research should combine web-tracking data with student self-report to provide more validation of results. In addition, collection of demographic data and disaggregation of noncredit student usage motivations would help further refining learning approaches for this growing population of online users.