Abstract:

In the field of education, identifying and assessing learner competencies are crucial for ensuring high-quality training. Skill mapping using semantic networks offers an innovative approach to achieving this goal. This method represents domain competencies as a network, highlighting their relationships and levels of complexity. Subsequently, this skill mapping allows for evaluating each learner's competency levels, identifying strengths and weaknesses, and proposing personalized learning activities to address knowledge gaps.

To what extent does skill mapping using semantic networks improve the assessment of learner competency levels and offer targeted learning activities to address knowledge gaps in a specific domain of learning?

The objectives of this study are as follows:
• Analyze the effectiveness of skill mapping based on semantic networks in evaluating learner competency levels.
• Evaluate the ability of skill mapping to identify knowledge gaps in learners.
• Examine the relevance and efficacy of targeted learning activities proposed by the skill mapping system to address identified gaps.
• Evaluate learner satisfaction and engagement towards this personalized learning approach.

To achieve these objectives, a mixed-methods methodology will be employed, combining quantitative and qualitative approaches. The key steps of the methodology are as follows:
• Data Collection: Select a representative sample of learners.
• Establishment of Skill Mapping: Identify key competencies in the chosen domain of learning and construct a semantic network linking competencies based on their interdependence and proximity.
• Learner Competency Evaluation: Design quizzes and assessments to measure learner competency levels in each skill represented in the semantic network.
• Quantitative Data Analysis: Statistically analyze evaluation data to assess the effectiveness of skill mapping in evaluating learner competency levels.
• Proposal of Targeted Learning Activities: Design a system for recommending personalized learning activities based on identified gaps for each learner.
• Qualitative Data Analysis: Conduct interviews with learners to gather their opinions and feedback on the skill mapping and proposed learning activities.
• Results Discussion: Interpret quantitative and qualitative results obtained from data analysis.

By combining quantitative and qualitative methods, this methodology will provide a comprehensive understanding of the effectiveness of skill mapping using semantic networks in the context of assessing learner competencies and personalizing the learning experience.

Keywords:

Semantic networks, Personalized Learning, Skill Development, Learning Experience.