Classification of Grain s and Quality Analysis u sing Deep Learning

There are various varieties of Rice and lentils. Price fabrication and adulteration have been some of the various issues faced by the consumers, farmers and wholesale retailers. Traditional methods for Identification of these similar types of grains and their quality analysis are crude and inaccurate. Methods were tried to implemented earlier but due to financial inability and low efficiency, they weren’t successful. To overcome this problem, the project proposes a method that uses a machine learning technique for identification and quality analysis of these grains. Rice and Lentils which have the maximum consumption have been selected. Lentils are designated into classes based on colors. The technique of determining the elegance of a lentil is with the aid of seed coat shade. Red lentils can be confirmed through the cotyledon coloration. Lentil types may also have a huge variety of seed coat colors from inexperienced, red, speckled inexperienced, black and tan. The cotyledon colour may be red, yellow or inexperienced. The size and color of every Indian Lentil type (i.e. Red, Green, and Yellow, Black, White) are decided to be large or Medium or small, then size and colour end up part of the grade name. An smart machine is used to perceive the kind of Indian lentils from bulk samples. The proposed machine allows kernel length and coloration size using picture processing techniques. These Lentil size measurements, when combined with color attributes of the sample, classify three lentil varieties commonly grown in India with the highest accuracy. Rice is one of most consumed grains in India so its quality is of utmost importance. In this project, we identify and grade five types of rice and grade them with the help of their distinguished features such as size, color, shape, and surface. The project works in three phases viz., Feature Extraction, Training, and Testing. Various rice grain has a different shape, size, surface and various lentils come in different colors, Hence the feature that will be extracted is texture and colors. The method of regression will be adopted for the grading mechanism where the output will be in terms of percentage purity. The methodology for the extraction of the feature will be GLCM and Edge Detection where for supervised learning SVM and Back Propagation will be utilized. The project provides an efficient replacement for the traditional grading mechanism and standardizes the pricing of farm products based on their quality only.

The Effect of Specifications Grading on Students’ Learning and Attitudes in an Undergraduate-Level Cell Biology Course

Specifications (specs) grading is a grading system in which mastery of specific educational outcomes is the basis for the final grade a student earns in the course. Implementation of the types of assessments used for specs grading has shown to be beneficial for student learning and motivation compared to traditional grading systems. We designed a specs grading strategy in an undergraduate Cell Biology course, creating 20 individual learning outcomes (LOs).

Replacing the Traditional Grading System with a System Emphasizing Student-facing Formative Assessment: A Mixed Method Study in Ninth Grade Mathematics Classrooms

We investigated the effects of replacing the traditional summative grading system with a system based on student-facing formative assessment: providing students feedback on their learning and opportunities to respond to the feedback; and basing final grades on learning students can demonstrate by the end of the marking period. Our mixed-methods study in ninth grade mathematics classrooms included a Cluster RCT, teacher interviews, and student surveys. We found a 0.31 standard deviation increase in algebra/geometry learning and positive impacts on students’ growth mindset, mastery goals, and peer interactions. The program did not impact students’ confidence, intrinsic value, or enjoyment of mathematics, but students with higher scores on these constructs benefitted more from the program than did students with lower scores.

Making the Grade during Pandemic: Early-stage and Late-stage Provisional Institutions

This is an article that draws on the institutional work literature about provisional institutions. To date, nearly every U.S. sector has been impacted by COVID-19. To sustain their core missions, highly institutionalized organizations such as universities have had to rethink foundational structures and policies. Using a historical ethnographic approach to investigate records from faculty senate deliberations at “Rural State University” (RSU), the authors examine the implementation of a temporary grading policy to supplement traditional, qualitative grades spring 2020 during the outbreak. The authors find that RSU implemented a temporary, supplemental grading policy as a provisional institution to momentarily supersede traditional grading as a means to—as soon as possible—return to it. This finding contrasts with the common understanding that provisional institutions operate primarily as a temporary solution to a social problem that leads to more stable and enduring, ostensibly nonprovisional institutions. The temporary grading policy, the authors argue, constitutes a “late-stage” provisional institution and, with this new lens, subsequently characterize the more commonplace understanding of provisional institutions as “early-stage.” This contribution has theoretical implications for studies of institutions and empirical implications for research on shared governance and disruption in higher education.

IMPLEMENTING COMPETENCY-BASED ASSESSMENT IN A FIRST YEAR ENGINEERING DESIGN COURSE

Competency-based assessment (CBA) is increasingly of interest in higher education, particularly in the professions. This assessment approach requires that students demonstrate satisfactory performance in specified measurable outcomes for progression, rather than meeting a particular overall average in a range of assessments. The paper describes the implementation of CBA in a first year engineering design and practice course. Each competency was linked to multiple assessment points to allow multiple opportunities to meet the competency without requiring the teaching staff to regrade assignments that did not meet expectations. The structure was designed to use no new additional course personnel. Based on this work the team recommends using past grades and learning outcome data the likely consequences of applying CBA to course assessment schemes, running CBA in parallel witha traditional grading scheme, and planning for flexibility in the grading scheme. Applying CBA to authentic activities was much more complex than for individual tests.

Choose Your Own Adventure: Gamified Course Design in History of Science

To combat high dropout rates and low motivation for online courses, we gamified a history of science course. To do so, we used an online educational program called 3DGameLab to convert what had been a well-liked face-to-face lecture and discussion course to an online format, for the purposes of long-distance teaching and learning. Within 3DGameLab, we prepared approximately three times as much content as would be taught in a face-to-face class. Clear tasks and immediate rewards in the form of experience points (XP) contributed to a transparent motivational system as compared to traditional grading. In this course, students completed their assignments asynchronously. Sustaining engagement is challenging in this format due to student self-management, but, with the game mode, students could repeat their attempts to pass a quest (a lesson) until they succeed (submit a passable response). The feedback cycle was short, and we found that students tend to persevere in the face of failure when they get rapid feedback, rather than quit. To test the adaptability of the asynchronous, gamified format, we also designed this course as a hybrid course. Students remained engaged when the feedback was quick, and the tasks were clearly set. We did not perform a quantitative study; the purpose of this article is to share a design study of our methods and subsequent experience with these modalities.

Points, Points, and More Points: High School Grade Inflation and Deflation when Homework and Employability Scores are Incorporated

With a strong movement of schools starting to use standards-based grading practices, one of the aims of this study was to learn if traditional grading practices communicate grades that are accurate based on the students’ learning of the course objectives. The purpose of this study was to determine the extent to which employability and homework scores within a traditional points- and percentages-weighted grading model inflates or deflates grades. This study analyzed 795 students’ semester math grades at an urban high school to see if, and to what extent, students’ grades were inflated or deflated due to including homework and employability scores in the grade. Final grades, which included homework and employability points, were compared to each student’s overall summative assessment scores to determine grade inflation or deflation. The study also analyzed how changing grading practices to eliminate homework and employability points would impact the number of students that ultimately passed or failed the course. Results of this study indicated 336 (43.2%) students had their grades inflated or deflated by 5% or more and 97 (12.6%) students had their grades inflated or deflated by 10% or more, which is equivalent to moving up or down a full letter grade. School leaders should consider separately communicating academic and non-academic factors to minimize grade inflation/deflation in order to make decisions based upon grades more justifiable.

Skills-Based Grading: A novel approach to teaching formal semantics

This paper reports an implementation of ‘Skills-Based Grading’ (SBG) in a formal semantics course. In traditional grading, every part of every assignment contributes to the final grade. Students are required to progress along a uniform timeline, with partial credit as a safety net. In SBG, by contrast, the course is composed of skills. Students are given multiple opportunities to demonstrate mastery in each skill, but full proficiency is required to gain credit. Zuraw et al. (2019) pioneered the use of SBG in linguistics for phonetics and phonology. SBG is known to work well for skills that require algorithmic approaches to arrive at inarguably correct answers. In applying SBG to semantics, we show that it is just as effective for more abstract and philosophical skills. Based on survey and grade data, we substantiate claims that SBG improves student learning, encourages more effective study, lowers student stress, and achieves more equitable outcomes. Since this paper reports our first use of SBG, we conclude with some reflections on improvements for the future.

Video Grading: Giving Students Personal Feedback in an Online World

Engaging students in a distance learning environment can be difficult. One method that can help professors bridge the gap is making the use of video grading. Grading via video note is an alternative to traditional grading of careplans, discussion boards and other assignments.