Timely and effective early warning is essential for proactive intervention to mitigate students’ learning risks. Existing studies on learning early warning primarily predict students’ knowledge mastery based on academic performance. However, they lack the assessment of course development objective achievement. According to the outcome-based education (OBE) concept, the course objective achievement serves as the foundation for comprehensive student assessment spanning knowledge acquisition, learning ability, and learning attitude. Using the course objective achievement as a guide for learning early warning can help to obtain more objective and accurate warning results. This article proposes a novel approach to learning early warning guided by course objective achievement via knowledge state and learning state modeling. This approach constructs knowledge states related to course objectives through a deep knowledge tracing model and derives the learning states comprising learning ability and learning attitude from multidimensional learning behavior data. The achievement state, fusing the knowledge and learning states, is then fed into a transformer model to capture the temporal dynamics of the achievement state and predict the achievement levels for each course objective. Based on these predictions, a four-level warning rule is employed to assess students’ learning risks. Experiments based on two real-world datasets demonstrate the effectiveness and superiority of the proposed approach. The approach provides a new research paradigm and a feasible solution to achieve accurate personalized early learning warning.

With the acceleration of globalization, the targeted international communication of national images contributes to enhancing a nation’s soft power and international recognition. It is challenging to select appropriate resources from the mass candidates for creating promotional works of national image. Existing research only focuses on the methodologies and lacks the systematic modeling and solving of national image resources recommendation. A collaborative recommendation approach to national image resources is proposed for targeted international communication. In it, the multidimensional features of national image resources and characteristics of communication audiences are modeled, and an evaluation mechanism is proposed to measure the comprehensive compatibility between national image resources and communication audiences. By innovatively introducing the role-based collaboration (RBC) theory and the environment-classes, agents, roles, groups, and objects (E-CARGO) model, the national image resources recommendation is formalized as a collaborative optimization problem. The mathematical model is built and solved via an optimization package. Finally, the case study and experiments show that the approach is efficient, feasible, and conducive to enhancing the efficiency of selecting national image resources. It offers a novel research paradigm for targeted international communication.

Teaching early warning is of great significance for avoiding teaching risks and continuously improving teaching quality. However, none of existing approaches assess the degree of course goals attainment and teacher's teaching quality from the perspective of cognitive diagnosis. This poses a challenge in providing accurate teaching early warning. This paper proposed an early warning approach for teachers based on cognitive diagnosis and long short-term memory (LSTM). First, this approach accurately evaluates students' cognitive status on knowledge concepts using a cognitive diagnosis model to assess their knowledge understanding degree and knowledge application ability. Second, the cognitive status on knowledge concepts is utilized to assess students' attainment degree of course goals and teachers' teaching quality. Third, the teachers' teaching quality is predicted in the future by using the LSTM network to mine students' learning process data, Finally, an accurate teaching early warning is provided to teachers based on a four-level early warning evaluation rule. In experiments, the real datasets are used and the results reveal that the proposed approach can accurately diagnose students' cognitive status and effectively predict teachers' teaching quality. This approach can provide an accurate teaching early warning service for teachers.

Recently, the city road trips have become one of the mainstream travel styles for Chinese tourists. Existing research does not comprehensively consider tourists' subjective preferences for sightseeing, dining, and accommodation, and analyze the objective constraints related to city road trips. It is difficult for tourists to obtain route planning solutions with high satisfaction of city road trips. A route planning approach for city road trips is proposed to meet these preferences and constraints. This approach defines two types of POIs (points of interest), i.e., attractions and rest spots, and identifies key constraints possibly affecting POI selections. Based on them, the tourist’s route for one day is divided into three sub-routes. Each sub-route consists of two different POIs including an attraction and a rest spot. First, an appropriate attraction is selected as the center of a sub-route. Second, a suitable rest spot is assigned to each sub-route according to its center. The E-CARGO model is utilized to formalize the route planning problem of city road trips and an effective solution is provided. Case study and simulation experiments show that the approach is efficient and feasible to achieve the maximum tourist satisfaction in city road trips.

It is important to select appropriate national image resources to build a nation’s images for targeted international communication of national images. Existing research only focusing on the methodologies, lacks the systematic modeling and solving of national image resources recommendation. A collaborative recommendation approach to national image resources is proposed. In the approach, an evaluation model of national image resources and an evaluation model of communication audiences are put forward, and an evaluation mechanism is proposed to measure the comprehensive compatibility between national image resources and communication audiences. By innovatively introducing the role-based collaboration (RBC) theory and the environment-classes, agents, roles, groups, and objects (E-CARGO) model, the national image resources recommendation is formalized as a collaborative optimization problem. The mathematical model is built and solved via an optimization package. Finally, the case study and experiments show that the approach is efficient, feasible, and conducive to enhancing the efficiency of national image resources recommendation. It offers a novel research paradigm for targeted international communication of national images.