## 3 Materials and Methods
### Dataset
#### 3.2.1 Dataset Preparation-Normalization  
Data preprocessing is essential for effective data analysis. It involves data preparation, normalization and cleaning to convert raw data into a machine-friendly format, addressing issues like missing values, outliers, and standardization. In this research, the preprocessing was conducted with RapidMiner Studio (version 10.1) operators and Python (version 3.12) was employed specifically for dataset creation and cleaning tasks. After data collection, important steps such as transcription, anonymization, segmentation into sentences, annotation and filtering classes are essential in preparing the text corpus for analysis (Figure 2). More specifically the process started with:  
1. Interviews transcription, capturing all elements of orality to ensure the richness and authenticity of the spoken content were preserved. The transcription of interviews was conducted carefully to ensure accuracy and preserve the integrity of the original content.
2. Anonymization and pseudonymization. Ensuring data privacy by eliminating any possibility of direct or indirect identification of personal data. Names were replaced with pronouns like \"\(\alpha\)\(\
u\)\(\tau\)\(\delta\)\(\varsigma\)\" (he) or \"\(\alpha\)\(\
u\)\(\tau\)\(\mu\)\" (she), or general nouns such as \"\(\pi\)\(\alpha\)\(\alpha\)\(\beta\)\" (child) or \"\(\delta\)\(\alpha\)\(\alpha\)\(\alpha\)\(\alpha\)\(\alpha\)\" (teacher).
3. Sentence segmentation. 1019 distinct sentences were identified by using end-of-sentence punctuation marks for demarcation. Segmentation into sentences was a preliminary step crucial for maintaining data integrity and enabling precise and efficient processing. This step was conducted to ensure a structured and organized dataset for analysis, preparing it for the annotation process.
4. Annotation. The data was annotated at the sentence level, divided into five topics based on their semantic content by two annotators (one internal and one external). The five predefined topics were: (i) \"Material and Technical Conditions\", (ii) \"Educational Dimension\", (iii) \"Psychological/Emotional Dimension\", (iv) \"Learning Difficulties and ERT\" and (v) \"Not Defined\". \"Not Defined\" topic was created for annotators to tag phrases or words lacking clear semantic content. This category was later excluded from the final analysis. The internal annotator was a member of the research team, closely involved with the project and possessing an in-depth understanding of the annotation guidelines. The external annotator was a qualified researcher in the field, though not directly involved with the research project, who annotated the data based on provided guidelines, directing them to classify sentences into designated categories based on their semantic content. To improve understanding and ensure accuracy in the annotation process, explicit examples for each classification category were provided to the annotators. Annotators achieved a 93% agreement rate. Some annotators disagreement examples were: (i) \"Excursor nojokes o o o e-class, \"\(\alpha\)\(\
u  
+++ ==WARNING: Truncated because of repetitions==  
++  
After removing the \"Not Defined\" topic, the sentence count dropped from 1019 to 996 (14,731 words). During the preprocessing phase, which included steps such as segmentation, tokenization, lemmatization, and removal of stop words (Section 4), the total count of sentences was reduced to 972. The distribution of sentences to topics (classes) is illustrated in Figure 3, showing data imbalance with 40% of the data belonging to topic \"Educational Dimension\". Hereafter, the remaining topics (classes) are referred to as: \"A: Material and Technical Conditions\", \"B: Educational Dimension\", \"C: Psychological/Emotional Dimension\", and \"D: Learning Difficulties and ERT\". From the above processes the Initial Dataset was formed.