Students will use digital tools (ML and Scratch) to analyze literary texts and extract relevant information.
“Machine Learning (ML) Applications"
1. A learning machine will be trained on a set of data (poems) to identify the verbs and pronouns in the first and second person singular
2. The lesson is based on pattern recognition in ML.
3. Engaging students by combining AI technology with literature
“Integration of Art and Technology”:
1. Using Machine Learning techniques to extract parts of speech such as verbs and pronouns in a text to identify the poetic self
2. The results obtained by analyzing the lyric text are compared with those obtained by using digital tools.
3. Encouraging creativity in coding, open discussions about poetry and technology, and hands-on activities.
4. Different lyric texts from national poets will be used
“Lesson Preparation and Evaluation”:
1. Pre-lesson: The notions of the poetic self and the ways of expressing it in poetry are reviewed.
2. Post-lesson: Students can create visualizations of the data obtained using tools such as Google Sheets
"Lesson Evaluation"
1.Participation in activities: Students' involvement in discussions and practical activities is evaluated.
2.Quality of analysis: Students' ability to correctly identify and interpret the elements that characterize the poetic self is evaluated.
3.Creativity in the use of digital tools: The originality and efficiency of the solutions found by the students to solve the tasks received are evaluated.
4.Team work: Students' ability to collaborate and assume responsibilities within the team is assessed.
To use digital tools (ML and Scratch) to analyze literary texts and extract relevant information.
To analyze the frequency of verbs and pronouns in the first and second person singular in a text, as an indicator of the degree of subjectivity.
To compare the results obtained by analyzing the lyric text with those obtained by using digital tools.
The notions of the poetic self and the ways of expressing it in poetry are reviewed using a spider web diagram
Practice the concept of ML in the analysis of verbs and pronouns of a poem.
1.Task:
Each team is looking for three poems from each of the three authors.
Students analyze the texts, identifying the elements that characterize the poetic self
Data Processing in Scratch:
-Students create a Scratch project in which they will insert lines from poems.
-Using code blocks in ML, students will extract keywords and calculate their frequency.
2.Organization:
Work in small groups to discuss and analyze patterns in language.
3.Differentiation:
Depending on the programming skills of the students, they will create models in ML and programming in Scratch.
basic level: the students collect 3 poems from the three authors and analyze the text with the aim of identifying the poetic self
level 1: using the ML model from the previous lesson to extract keywords and calculate their frequency.
level 2: creating a Scratch project in which they will insert lines from the poem.
Link to Objectives:
Students identify the type of poetic self by analyze the results obtained from the ML model
Comparison of results: compare the results obtained by analyzing the lyric text with those obtained by using digital tools.
The teacher provides feedback highlighting strengths and weaknesses.
Sharing:
Discussion: An open discussion about the advantages and disadvantages of each analysis method.
Next Lesson:
Introduction in the next lesson - To use digital tools such as Scratch to create interactive projects related to a lyric text.
Evaluation
Data visualization: each student creates visualizations of the data obtained, using tools such as Google Sheets
Attach example documents and jpeg of artefact.
Apps: Scratch, Machine Learning for Kids platform
Equipment: Computers with internet access, projector for demonstrations.
Supplementary: Printed worksheets on basic ML concepts, example poems for reference.