Peer grading is widely used in high education as effective active learning but still faces challenges. We present the peer grading approach for Open-ended Programming Projects based on the binary and Swiss systems. First, we design a grading specification to improve the accuracy of scoring. Second, to make grading easier for inexperienced students, we utilize a pairwise comparison system based on the binary system. Third, we propose a score calculation algorithm based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to improve grading accuracy. We developed an online peer review tool called Peer Review Studio (PRS) based on the approach. We carry out the method in the undergraduate programming course of 2023. We collect and analyze the learning data between 2022 and 2023. When measured by Krippendorff's alpha, the inter-rater reliability between instructor and peer grading is in good agreement. When measured by Kruskal-Wallis, students' project performance and learning engagement significantly improve in the first year of peer grading. The course questionnaire 2023 reveals that most students hold a positive attitude toward peer grading and have benefited significantly from this approach.

This research to practice WIP paper presented a Blended Learning (BL) approach to teaching Data Structures and Programming in Python (DSPP) course. This approach increased students' learning engagement, academic performance, and course satisfaction. There is an intense need to study the BL approach in higher education, especially during the COVID-19 epidemic. Although BL pedagogy has achieved great success in college, there needs to be more quantitative research and practice in the computer programming course. This paper takes the DSPP course as an example to introduce the BL approach in detail. First, we designed the BL process for the computer programming course, which includes specific pre-class, in-class, and after-class activities. Second, we divide a course's knowledge and BL activities into remember, understand, apply, analyze, evaluate, and create levels according to Bloom's Taxonomy. So the knowledge and activities can be corresponding according to the cognitive level. We take a chapter in the DSPP course as an example to introduce it in detail. Third, to support the efficient implementation of BL activities, we have designed and developed several online learning systems, including an Online Judge system (OJ), Question&Anwser website (Q&A), and Peer Review Studio (PRS). To assess the effectiveness of the BL approach, we conducted the study at the DSPP course for undergraduate students in 2021 and 2023. In 2021, we taught the DSPP course in traditional pedagogy. In 2023, we began to implement BL pedagogy. By analyzing students' learning data, we compared the effect of the two pedagogy. We use the nonparametric independent-samples Kruskal-Wallis test to measure the changes in learning engagement, course performance, and student satisfaction. The results show that they were significantly improved in 2022. The course questionnaire of 2022 shows that most students prefer the BL approach.

This research to practice full paper presented a peer review approach to grading projects in computer courses. Educators commonly adopt project-based learning activities in computer courses to achieve the goal. Implementations of these projects typically involve hardly quantifiable dimensions, such as novelty, functionality, user-friendly, coding style, and document. Automated tools are not competent for project assessment. However, assessing projects by instructors can be labor intensive and generally involves a high degree of subjectivity. Peer review is widely used in high education as effective active learning in computer courses, especially in the past decade. Despite its many advantages, peer review still faces some challenges. Students need to be more expert in assessment and might make mistakes when grading, and instructors still need to make great efforts to supervise the process and ensure that students give fair scores to their peers. In addition, the social relationship between students could lead to subjective assessments and affect the fairness of grading. We present the peer review approach to grading projects in computer courses. First, we design a grading specification to improve the accuracy of scoring. Second, we propose a score calculation method based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to calculate the assignment's final score, which is a multi-objective decision-making problem. Third, we give an anomaly detection algorithm based on Dixon's Q test to filter peer reviewers' unreasonable scores. Based on the approach, we designed a peer review workflow and developed an online peer review tool called Peer Review Studio (PRS). We carry out the method in the undergraduate computer course of 2022. We collect and analyze the learning data between 2020 and 2022. When measured by Krippendorff's alpha, the inter-rater reliability between instructor and peer grading is in good agreement. When measured by Kruskal-Wallis, students' project performance is significantly improved in the first year of peer review. Although learning engagement increases in 2022, there is no statistically significant difference with 2021. The course questionnaire of 2022 shows that most students approve of the peer review approach.

This Research to Practice Full Paper presented a Flipped Classroom (FC) approach to teaching a computer programming course. This approach increased students’ academic performance, course satisfaction and learning motivation by providing more time for active learning.There is an intense need for the studies of FC approach in higher education, especially in computer programming courses. Although FC pedagogy has achieved great success in K-12 schools, there is rare quantitative research on programming courses in higher education.This paper introduced a FC approach to teaching computer program course, and described in detail how to arrange pre-class activities and in-class activities. Pre-class activities were recommended to be arranged by time. 5 days before class, release course material and open access to programming practice. Students began to study and practice by themselves. 2 days before class, Students were required to complete pre-class test. 1 day before class, the teaching assistant provided a pre-class test analysis report. The lecturer prepared the coming class based on the report. In-class activities consisted of four parts. First, the lecturer explained in detail the common problems in the pre-class test. Second, the lecturer checked the problem-solving by asking questions. Third, students discussed in teams. Fourth, students gave presentations on the advanced topics.To assess the effectiveness of the FC approach, the study was conducted at the course Swift Language Programming Practice for undergraduate students. Students were divided into two groups. One group implemented traditional pedagogy, and the other group implemented FC pedagogy. The effect of the two groups was analyzed by teaching data collected during 2019-2021. The nonparametric independent-samples Kruskal–Wallis test was used to measure the changes in course performance and student satisfaction. The results showed that by introducing FC to teach computer programming courses, course performance (measured by examination results) and student satisfaction (measured by course questionnaires) were significantly improved.The contribution of this paper was to propose a FC approach suitable for college programming course, and through quantitative statistical methods to analyze the teaching effect of the course. The results showed that the approach significantly improved the course performance and student satisfaction. The FC approach of this paper can be regarded as a reference for similar course in college.

This Research to Practice Full Paper presented a comprehensive experiment approach to enhancing computer engineering ability. This approach integrated Swift programming language, iOS development, UML, software testing, MVC, Cocoa Touch Framework and Design Patterns into a comprehensive experiment, through which students can master the engineering methods to solve complex application problems.In college, traditional computer programming courses focus on the grammar and classical algorithm of programming language. Usually the amount of code is far lower than that of industrial products. Such programming courses can’t effectively improve students’ ability to solve complex engineering problems. They also can’t meet the requirements of industrial development. Students are not satisfied with the results of these courses. There is an intense need for the studies of enhancing student’s computer engineering ability.Taking Swift Language Programming course as an example, this paper presented a comprehensive experiment approach to enhancing students’ computer engineering ability by developing classic industrial iOS Apps.Flipped classroom pedagogy is conducive to free much time in class. Lecturers can fully communicate with students and help students complete challenging tasks. The comprehensive experiment consists of pre-class activities and in-class activities. Before class, the lecturer provides experiment materials online including theoretical handouts of Design Patterns, manuals of UML 2.0 specifications and Cocoa Touch reference manual, etc. Students learn the materials by themselves, practice and discuss online and complete the corresponding pre-class tests. In class, the lecturer analyzes in detail the problems students encounter after class and guides them to solve these problems. The lecturer also participates in each group discussion to ensure the smooth progress of students’ project.The implementation of comprehensive experiment is divided into four sub tasks. These tasks are app function analysis, App detailed design, programming implementation, and App release and launch. First, according to the requirements of the App, the function is analyzed in detail and defined with UML. Second, based on functional analysis, the App’s system architecture, data structure, view combination, logic execution process and core algorithms are designed. The system is defined in detail with UML Class diagram. Third, according to the detailed design of the App, user interface is built by Xcode storyboard, and the model layer, view layer and control layer are implemented in Swift. Then unit test and system test are conducted on the App and bugs are repaired. Finally, App launch is completed including App internationalization, developer certificate applying, creating description file, setting product identification and deployment information, and submitting App online.To assess the effect of this comprehensive experiment approach, three-year teaching data were analyzed using statistical methods. The results show that students’ engineering ability (measured by code scale) and student satisfaction (measured by questionnaires) were significantly improved.Our contribution is to propose a detailed comprehensive experiment approach to enhancing computer engineering ability. The analysis of teaching data show that it is helpful to improve students’ computer engineering ability and course satisfaction.

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N3) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN3/k). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.

In optical flow estimation task, coarse-to-fine warping strategy is widely used to deal with the large displacement problem and provides efficiency and speed. However, limited by the small search range between the first images and warped second images, current coarse-to-fine optical flow networks fail to capture small and fast-moving objects which has disappeared at coarse resolution levels. To address this problem, we introduce a lightweight but effective Global Matching Component (GMC) to grab global matching features. We propose a new Hybrid Matching Optical Flow Network (HMFlow) by integrating GMC into existing coarse-to-fine networks seamlessly. Besides keeping in high accuracy and small model size, our proposed HMFlow can apply global matching features to guide the network to discover the small and fast-moving objects mismatched by local matching features. We also build a new dataset, named SFChairs, for evaluation. The experimental results show that our proposed network achieves considerable performance, especially at regions with small and fast-moving objects.