This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning.

This study addresses the challenges faced in personalized tutoring within large-scale programming courses, such as significant ability gaps among students, limited available resources, among others. For these reasons, we proposed an intelligent programming assistant, ProgMate, based on large language models (LLMs). Benefitting from the robust understanding and learning capabilities of LLM, ProgMate not only comprehensively monitors the learning process, but can also surpass human teaching in aspects such as intelligent assignment grading, identification of knowledge gaps, and assessment of learning abilities. ProgMate embodies a new 4-A digital teaching paradigm, characterized by its ability to provide precise guidance with anything, to anyone, anywhere, at any time, with features like “omnipresence”, “adaptive guidance” and “customization”. It facilitates the organic integration of collective teaching and individualized guidance, continuous learning, and long-term development, as well as resource efficiency and precision nurturing, offering a viable path and empirical support for the digital transformation of future education.

Pretrained large language models (LLMs) exhibit exceptional general language processing capabilities but come with significant demands on memory and computational resources. As a powerful compression technology, binarization can extremely reduce model weights to a mere 1 bit, lowering the expensive computation and memory requirements. However, existing quantization techniques fall short of maintaining LLM performance under ultra-low bit-widths. In response to this challenge, we present BiLLM, a groundbreaking 1-bit post-training quantization scheme tailored for pretrained LLMs. Based on the weight distribution of LLMs, BiLLM first identifies and structurally selects salient weights, and minimizes the compression loss through an effective binary residual approximation strategy. Moreover, considering the bell-shaped distribution of the non-salient weights, we propose an optimal splitting search to group and binarize them accurately. BiLLM, for the first time, achieves high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLM families and evaluation metrics, outperforms SOTA quantization methods of LLM by significant margins. Moreover, BiLLM enables the binarization process of a 7-billion LLM within 0.5 hours on a single GPU, demonstrating satisfactory time efficiency. Our code is available at https://github.com/Aaronhuang-778/BiLLM .

In order to cultivate students' software and hardware collaborative design thinking and system construction ability, this paper proposes an innovative experimental teaching method of software and hardware collaborative design to better develop students'sys-tem view. We create a student-centered learning environment and adopt a mixed teaching method whereby experiment courses are conducted in a “traditional + discussion + scientific research-driven” manner. The course will focus on the design of the “Convolutional Neural Network for Handwritten Digit Recognition”, and run through curriculums that involve from software to hardware, algorithms to systems, and design to verification. We explore teaching methods that revolve around principles of using experiments to derive theories, and using theories to guide experiments. Besides, we establish an online laboratory that can provide an experiment platform (MOOE) for “PC+ARM+FPGA”, which realizes the seamless integration of physical operations, semi-physical emulators, and virtual models. Through investigation and research, most students who have completed this course have formed a good knowledge of computer systems and the necessary comprehensive development ability.

The development of learning analytics technology has created the necessary conditions for the implementation of large-scale personalized education. By taking advantage of the current advancements available, we can adopt a deep integration of “artificial intelligence + education”, take educational big data as the research object, aim at achieving precise teaching, and use cognitive diagnosis as a means to construct an intelligent guidance model. The model will conform to a learner's cognitive patterns and curriculum characteristics to meet his personalized learning needs under different knowledge states. Firstly, use knowledge map and abstract syntax trees to extract the characteristics of teaching objectives from questions and standard programing codes. Secondly, Use “target characteristics - learning characteristics - learning sequence - learning effect” cognitive diagnosis model to assess student achievement in learning. Finally, according to the correlation between knowledge points, the initial learning path consisting of all relevant knowledge points is identified from a knowledge map. And the most optimal learning path is selected based on the importance of knowledge points and student's learning situation. There are three innovation points in this paper. First, the traditional method which only focuses on discovering the one-dimensional knowledge features implicated in the questions, is extended to construct a knowledge-ability multidimensional feature matrix. Secondly, it optimized static cognitive diagnosis into dynamic cognitive arbitrariness, which has a modeling capability of dynamic temporal sequence. Thirdly, it regarded the correlation between knowledge points, and the importance of knowledge points as the heuristic conditions for constructing a learning path.

Aiming to solve learning difficulties caused by information explosion in the current era of education informatization, this paper proposes a precise teaching model driven by knowledge graphs. Knowledge graphs are essentially learning tools that effectively build a correct and complete curriculum knowledge system and accurately promote personalized learning paths. The main research contents are: (1) In view of the problem that the general knowledge graphs currently in use are not applicable in the field of education, we define the knowledge graph ontology structure of special courses based on Bloom’s teaching target system; (2) In view of the diversification of various teaching data sources, a simple knowledge graph representing structured data is used as a heuristic condition to extract a complete teaching sequence between knowledge points through self-expansion. (3) In view of the problem faced when trying to navigate relevant knowledge points in personalized learning, this paper exploits the internal relationship between knowledge point loopholes and ability achievements, and constructs an accurate path recommendation model based on quantitative data analysis. Taking the C language programming course as experimental data, this paper verifies the effectiveness of this model by quantitative means, which can significantly provide accurate teaching quality and realize the "multi-directional adaptation" among teachers, courses and students.

Computer system ability training is a new trend in computer education. This paper proposed an innovative experimental teaching method of software and hardware collaborative design to better develop students' system view, structure view, engineering view of computers. An FPGA-based CNN accelerator was designed to combining the knowledge from software to compilation and then to hardware. The main innovations are: (1) Innovation of experimental system: A “curriculum tree” based on knowledge map was built to identify the implicit relationship between software and hardware knowledge. The curriculum was changed from Horizontal Teaching to Vertical Teaching in order to reduce the difficulties of cultivating system ability; (2) Innovation of experiment contents: it proposed an experiment teaching strategy of “Managing Complexity With Simplicity” guided by Occam's razor and used some effective methods to simply the system knowledge of each course around the top-level goals; (3) Innovation of experiment methods: a procedural and flow-based experiment model based on hierarchical experimental contents was used to achieve spiral progressive learning from software design to hardware simulation and then to system development; @Innovation of experiment platforms: an innovative method of conducting experiments, MODE (MODE = Experiment + MOOC) was proposed to allow students to do experiments “anytime, anywhere and on demand”.

This paper aims to study the new changes in higher education in the post COVID-19 era and proposed an innovative Online-to-Offline (O2O) blending teaching model, named BOPPPS-SPOC by tracking a C programming language course. BOPPPS-SPOC indicates implementing BOPPPS mode for SPOC (Small Private Online Course). BOPPPS is an acronym for 6 components: Bridge in, Objective, Preassessment, Participatory learning, Post-assessment and Summary and it makes the classroom teaching arrangement more logic and rationality. The new changes are: (1) Teaching Methods: From separated online and offline teaching to an online-offline blending teaching. By adopting the new BOPPPS-SPOC method, the traditional offline and online independent teaching, implemented in a serial way, is optimized into offline and online blending teaching, implemented in a cross-parallel way, and it effectively expand the time and space scope of classroom teaching; (2) Course Evaluation: From single-dimensional evaluation to multi-dimensional evaluation. It focuses on the construction of a fusion evaluation system, mainly including the formative evaluation of teaching process and the sum. Finally, a survey from an authoritative research institution is conducted to 334 universities in China. It shows about 95% of students are able to adapt to an O2O blending teaching.

This Research-to-Practice Work-In-Progress paper proposed a multidimensional ability-oriented approach for teaching program with the integration of outcome-oriented, student-centered, project-based and contest-driven. The main contributions were: (1) proposed an Ability-Driven Programming Model (ADPM) from two-dimensions of knowledge taxonomy and practice taxonomy which refined Bloom Taxonomy and defined computer programming ability from four hierarchical levels, they are basic ability, comprehensive ability, engineering ability and innovative ability; (2) designed an improved student-centered pattern to reconstructed contents to make each topic associated with six objectives in Bloom's taxonomy and explored some facts to verify the effect of both student-centered teaching and learning; (3) adopted a project-driven method based on "Conceive-Design-Implement-Operate" to solve complex engineering problems and elaborated on how to design good projects and how to measure the quality of projects in detail. Finally, analysis conducted using survey questionnaires and classroom videos indicates that use of Ability-Driven methodology has motivated students to become active learners and improved engineering practice ability. The Ability-Driven methodology provides an applicable model for a student-centered teaching pedagogy to cultivate students' engineering habits and teach them to think like an engineer.

This Research-to-Practice Work-In-Progress paper proposes a state-of-the-art method of "hardware-software co-design" (HSC) based on FPGA. The main contributions were: (1) Optimizing Curriculum System from hierarchical structure to vertical structure. The traditional computer courses are taught horizontally and independently which ignores the connection between software and hardware. Therefore, we adopt a coherent curriculum and teaching is vertically structured and logically sequenced to reconstruct the contents from loose coupling to tight coupling; (2) Optimizing teaching process from Software-Hardware to Hardware-Interface-Software. We establish a closed-loop teaching framework by designing some tightly coupled projects to integrate hardware, interface and software together; (3) Optimizing teaching method from complex to simple. Complex teaching tries to entirely develop a real system in one time but it is too difficult to implement. Based upon the theories of Occam's razor and Separation of Concern, simple teaching eliminates unnecessary knowledge and decouples the complex system into single and simple modules; (4) Optimizing teaching objectives from solving basic academic problems to solving complex engineering problems. To train engineering talents, we use industrial methods to solve industrial problems which meet industry standards. Finally, evaluation based on a capability-maturity model like CDIO-CMM (CDIO Capability-Maturity Model) was done by means survey questionnaire and the results indicate hardware-software co-design can effectively improve students' ability of system design and the proportion of students at advanced level is increased from 13% to 37%.

The fourth industrial revolution (Industry 4.0) has promoted the all-around transformation of education in engineering. To cope with the changes caused by Industry 4.0, the Ministry of Education of China announced a new strategic guideline named “Emerging Engineering Education”, which proposed new demands of C programming courses. It is required to pay more attention to cultivate students' engineering practice ability. However, currently there is a phenomenon of “disconnection between teaching and application” behaving as some educators focus on teaching knowledge, but ignore training students' engineering skills, engineering thinking and engineering accomplishment. In order to solve these problems, we establish a new engineering-oriented curriculum system to cultivate interdisciplinary talents with strong engineering practice ability by designing innovative teaching pattern, advanced teaching methods and optimized teaching goals. The main measures are: 1) To construct a three-in-one curriculum system of “experiment, practice and internship”. It aims to implement engineering-oriented teaching strategies by designing course contents guided by enterprise needs and taking project as the carrier. 2) To construct an engineering-oriented teaching pattern of “multiple subjects, double tutors”. It wants to establish a netlike teaching model including teaching community of “teacher-student”, learning community of “student-student”, practice community of “student-enterprise” and guiding community of “intramural advisor-extramural advisor”. 3) To construct project-driven teaching contents. It commits to design a teaching chain of “knowledge + experiment + project” following the learning rule of “from perceptual-knowledge to rational-knowledge to practice-knowledge”. In conclusion, this paper proposed a new engineering application-oriented curriculum system, which takes engineering projects as cases, takes the cycle of project development as main-line and takes the cultivation of engineering talents as goals.

This paper proposed a new kind of online education mode, MOOE (Massive Open Online Experiment). MOOE was produced based on the thoughts of the Internet thinking and the opening-and-sharing resources and was the organic combination of MOOC and experimental teaching. The main contributions were made: (1) Instructed MOOE's concept and characteristics. MOOE build a network laboratory with the advanced information technology to make experimenters complete all the experimental activities through the Internet without the limitation of time, space and resources. (2) Analyzed the similarities and differences between MOOE and MOOC. MOOE was the extension and expansion of MOOC, which realized the online learning of all the teaching activities including the theoretical courses and their related experiments. MOOE inherited the advantaged of MOOC, but paid more attention to the virtualization of experimental equipment, operations and contents. (3) Described the implement and application of MOOE by taking an experimental course titled “FPGA Based Multi-core Computing” as an example. The feasibility and advantage of MOOE were proved by the actual statistical data of the course.

Scheduling of real-time tasks on a multi-core processor is challenging due to the execution time being a nondeterministic value. Through studying the relationship between task affinity and execution time, we propose an accelerated multi-core real-time scheduling algorithm (RM-λ) for periodic and dependent real-time tasks on a homogeneous multi-core processor based on acceleration between tasks to obtain a real-time scheduling scheme with less resource utilization. We adopt an acceleration factor matrix to represent the degree of affinity and develop a real-time scheduling model to find the best accelerated pair. The heterogeneous multi-core architectures can execute tasks by sharing their dependent data on L1 Cache. The results demonstrate our approach can loosens the schedulability constraints of RM (maximum improvement of 25%) so that an un-schedulable real-time tasks set on a single-core processor might be schedulable, and for those still hard to be scheduled, could be made schedulable on a multi-core processor.

With the wide use of computers, how to make students understand the special way of thinking of computer science and find the appropriate methods to solve problems in their own fields like computer scientists are the challenges for educators. This paper proposed a computational thinking model based on blended learning Mctbbland a descending dimension method for problem-space transformation. The Mctbblmodel analyzes and researches the application of computational thinking in computer science by the organic combination of computational thinking and blended learning in a life-cycle perspective. On one hand, the model can summarize the core concepts and important principles in computer science and extract the typical thoughts and general methods of solving problems; on the other hand, the model can implement the teaching and learning of computational thinking by means of blended learning. Based on the model, the teachers can teach the problem-solving process by using computer more intuitively and clearly and the students can understand the application of computer knowledge more easily and simply. The Mctbbl prolongs the study life cycle which takes preview as beginning, teacher-student discussion as body and practical application as core to ensure integrity, endurance and consistency of teaching.