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LLMs for Educational Assessment

Scope: Evaluate the effectiveness and reliability of LLMs in automating assessment of anonymized summer school applications through direct comparison with human evaluations.

Provide actionable recommendations on implementation feasibility and contexts favoring human versus LLM-based evaluation.

Importance: Identify biases and inconsistencies in human assessment while revealing fundamental differences between human and LLM evaluation approaches, enabling systemic improvements in educational evaluation practices.

Data overview

The raw data is saved into /data/raw folder. It contains anonymized entries (CVs, Motivational letters and Presentation texts) from applicants and their scores. The initial data is spreaded among several subfolders with .txt files. The applicants' scores and their final marks are saved in several .csv files that have been created at various stages of selection process and during the school.

The preprocessed and compressed dataset is saved as /data/dataset.parquet file with all necessary data for each student, who have either submitted CV, Motivational letter or Presentation. The dataset contains 577 rows and 25 columns in total. The table below gives explicit description of the dataset content.

# Column Non-Null Count Dtype Range Description
0 cv 567 non-null object N/A The anonymized CV in the form of string.
1 letter 568 non-null object N/A The anonymized Motivational letter in the form of string.
2 presentation 564 non-null object N/A The anonymized text from Presentation slides in the form of string.
3 cv_phd_1 567 non-null float64 0 - 5 The CV score from first PhD student assessor.
4 cv_phd_2 567 non-null float64 0 - 5 The CV score from second PhD student assessor.
5 letter_phd_1 567 non-null float64 0 - 5 The Motivational letter score from first PhD student assessor.
6 letter_phd_2 567 non-null float64 0 - 5 The Motivational letter score from second PhD student assessor.
7 pres_phd_1 567 non-null float64 0 - 20 The Presentation score from first PhD student assessor.
8 pres_phd_2 567 non-null float64 0 - 20 The Presentation score from second PhD student assessor.
9 pres_class 567 non-null float64 0 - 3 The type of Presentation (review, article replication of personal project).
10 video_phd_1 383 non-null float64 0 - 20 The Presentation video score from first PhD student assessor.
11 video_phd_2 409 non-null float64 0 - 20 The Presentation video score from second PhD student assessor.
12 all_phd_1 567 non-null float64 0 - 100 The total score from first PhD student assessor.
13 all_phd_2 567 non-null float64 0 - 100 The total score from first PhD student assessor.
14 final_human_score 98 non-null float64 0 - 100 The final score (weighted mean of PhD students and Professor scores).
15 prof_score 98 non-null float64 0 - 10 The Professor's overall score.
16 offline_test_1 48 non-null float64 0 - 1 The results of the first offline test.
17 offline_test_2 48 non-null float64 0 - 1 The results of the second offline test.
18 offline_test_3 48 non-null float64 0 - 1 The results of the third offline test.
19 offline_test_4 48 non-null float64 0 - 1 The results of the fourth offline test.
20 offline_test_total 48 non-null float64 0 - 1 The sum of all offline tests.
21 online_test_score 68 non-null float64 0 - 67 The results of the online test
22 project_participation_flag 577 non-null bool T/F The flag of student's participation in the project activity.
23 project_ta_score 118 non-null float64 0 - 5 The project score from the teacher assistants.
24 project_peer_score 118 non-null float64 0 - 10 The project score from the peer-review stage.

The detailed data description is provided in /preprocessing.ipynb.

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