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junyi数据集说明

data source

Authorization

Any form of commercial usage is not allowed!
Please cite the following paper if you publish your work:

Haw-Shiuan Chang, Hwai-Jung Hsu and Kuan-Ta Chen,
"Modeling Exercise Relationships in E-Learning: A Unified Approach,"
International Conference on Educational Data Mining (EDM), 2015.

Introduction

The dataset contains the problem log and exercise-related information on the Junyi Academy ( http://www.junyiacademy.org/ ), an E-learning platform established in 2012 on the basis of the open-source code released by Khan Academy. In addition, the annotations of exercise relationship we collected for building models are also available.

Meaning of Fields

junyi_Exercise_table.csv:

字段名 说明
name Exercise name (The name is also an id of exercise, so each name is unique in the dataset). If you want to access the exercise on the website, please append this name after url, http://www.junyiacademy.org/exercise/ (e.g., http://www.junyiacademy.org/exercise/similar_triangles_1 ). Please note that Junyi Academy are constantly changing their contents as Khan Academy did, so some url of exercises might be unavaible when you access them.
live Whether the exercise is still accessible on the website on Jan. 2015
prerequisite Indicate its prerequisite exericse (parent shown in its knowledge map)
h_position The coordiate on the x axis of the knowledge map
v_position The coordiate on the y axis of the knowledge map
creation_date The date this exercise is created
seconds_per_fast_problem The website judge a student finish the exercise fast if he/she takes less then this time to answer the question. The number is manually assigned by the experts in Junyi Academy.
pretty_display_name The chinese name of exercise shown in the knowledge map (Please use UTF-8 to decode the chinese characters)
short_display_name Another chinese name of exercise (Please use UTF-8 to decode the chinese characters)
topic The topic of each exercise, and the topic would be shown as a larger node in the knowledge map.
area: The area of each exercise (Each area contains several topics)
  • Example
name live prerequisites h_position v_position creation_date seconds_per_fast_problem pretty_display_name short_display_name topic area
parabola_intuition_1 TRUE recognizing_conic_sections 47 2 2012-10-11 17:55:24.8056 UTC 13 ?物線直覺 1 ?物線直覺1 conic-sections algebra
circles_and_arcs TRUE 40 -20 2012-10-11 17:55:33.41014 UTC 27 圓與弧 圓與弧 area-perimeter-and-volume geometry

relationship_annotation_training.csv / relationship_annotation_testing.csv

字段名 说明
Exercise_A, Exercise_B The exercise names being compared
Similarity_avg, Difficulty_avg, Prequesite_avg The mean opinion scores of different relationships. This is also the ground truth we used to train/test our model.
Similarity_raw, Difficulty_raw, Prequesite_raw The raw scores given by workers (delimiter is "_")
  • Example
Exercise_A Exercise_B Similarity_avg Similarity_raw Difficulty_avg Difficulty_raw Prerequisite_avg Prerequisite_raw
radius_diameter_and_circumference arithmetic_word_problems_1 1.857142857 1_4_1_1_1_1_2_1_1_1_3_1_3_5 2.857142857 4_5_1_1_1_1_7_1_1_4_2_5_2_5 3 1_6_1_1_1_3_2_1_9_2_3_2_8_2
radius_diameter_and_circumference parts_of_circles 6.785714286 6_9_6_6_7_8_7_8_8_8_4_6_5_7 2.428571429 3_5_1_3_2_1_5_1_1_1_1_2_5_3 7.285714286 6_7_7_6_8_8_9_5_9_9_7_7_5_9

junyi_ProblemLog_original.csv

字段名 说明
user_id An number represents an user
exercise Exercise name
problem_type Some exercises would record what template of problem this student encounters at this time
problem_number How many times this student practices this exercise (e.g., the number would be 1 if the student tries to answer this exercise at the first time)
topic_mode Whether the student is assigned this exercise by clicking the topic icon (This function has been closed now)
suggested Whether the exercise is suggested by the system according to prerequisite relationships on the knowledge map
review_mode Whether the exercise is done by the student after he/she earn proficiency
time_done Unix timestamp in microsecends
time_taken Second the student spend on this exercise
time_taken_attempts Seconds the student spend on each answering attempt
correct Whether the student's first attempt is correct, and the field would be false if any hint is requested
count_attempts How many times student attempt to answer the problem
hint_used Whether student request hints
count_hints How many times student request hints
hint_time_taken_list Seconds the student spend on each requested hints
earned_proficiency Whether the student reaches proficiency. Please refer to http://david-hu.com/2011/11/02/how-khan-academy-is-using-machine-learning-to-assess-student-mastery.html for the algorithm of determining proficiency
points_earned How many points students earn for this practice
  • Example
user_id exercise problem_type problem_number topic_mode suggested review_mode time_done time_taken time_taken_attempts correct count_attempts hint_used count_hints hint_time_taken_list earned_proficiency points_earned
12884 time_terminology analog_word 1 false false false 1420714810324490 4 3&1 false 2 false 0 null false 0
239464 multiplication_1 0 6 false false false 1403098400836660 2 2 true 1 false 0 null false 14

junyi_ProblemLog_for_PSLC.csv

The tab delimited format used in PSLC datashop, please refer to their document ( https://pslcdatashop.web.cmu.edu/help?page=importFormatTd ) The size of the text file is too large (9.1 GB) to analyze using tools of websites, so we compress the text file and put it as an extra file of the dataset. We also upload a small subset of data into the website for the illustration purpose. Note that there are some assumptions when converting the data into this format, please read the description of our dataset for more details.

  • Example
Anon Student Id Session Id Time Student Response Type Tutor Response Type Level (Unit) Level (Section) Problem Name Problem Start Time Step Name Outcome Condition Name Condition Type Selection Action Input KC (Exercise) KC (Topic) KC (Area) CF (points_earned) CF (earned_proficiency)
12884 148691 1420714809324 ATTEMPT RESULT telling-time time_terminology time_terminology--analog_word 1420714806324 time_terminology--analog_word INCORRECT Choose_Exercise NA NA NA NA time_terminology telling-time arithmetic 0 0
12884 148691 1420714810324 ATTEMPT RESULT telling-time time_terminology time_terminology--analog_word 1420714809324 time_terminology--analog_word INCORRECT Choose_Exercise NA NA NA NA time_terminology telling-time arithmetic 0 0
239464 93497 1403098400837 ATTEMPT RESULT multiplication-division multiplication_1 multiplication_1--0 1403098398837 multiplication_1--0 CORRECT Choose_Exercise NA NA NA NA multiplication_1 multiplication-division arithmetic 14 0

Questions and Collaboration:

1. If you have any question to this dataset, please e-mail to hschang@cs.umass.edu.
2. If you have intention to acquire more data which fit your research purpose, please contact Junyi Academy directly for discussing the further cooperation opportunites by emailing to support@junyiacademy.org

Note:

1. The dataset we used in our paper (Modeling Exercise Relationships in E-Learning: A Unified Approach) is extracted from Junyi Academy on July 2014, and this dataset is extracted on Jan 2015. After applying our method on the new dataset, we got similar observation with that in our paper, even though this dataset contains more users and exercises. 
2. After uncompress the original problem log and problem log using PLSC format, the text files will take around 2.6 GB and 9.1 GB respectively. Please prepare enough space in your disk.

Annotaion:

  1. PSLC数据集是对original数据集做了处理以后生成的数据，拆分的字段为time_taken_attempts，因此PSLC数据集的条目数比original的多

Analysis

每个用户的练习次数及对应的知识点数(50000 session 抽样)

exercise_length exercise_num
count 8246.000000 8246.000000
mean 167.808513 9.569367
std 616.725544 21.860770
min 1.000000 1.000000
25% 7.000000 1.000000
50% 19.000000 3.000000
75% 85.000000 9.000000
90% 335.000000 23.000000
max 16111.000000 517.000000

每个用户的session数(50000 session 抽样)

session_num
count 8246.000000
mean 6.063789
std 18.974000
min 1.000000
10% 1.000000
25% 1.000000
50% 1.000000
75% 4.000000
90% 12.000000
max 521.000000

每个session对应的练习次数、知识点数、session的近似持续时间(50000 session 抽样)

exercise_length exercise_num last_time
count 50002.000000 50002.000000 50002.00000
mean 27.673873 2.833487 386.93766
std 42.860613 3.816037 518.76202
min 1.000000 1.000000 0.00000
10% 1.000000 1.000000 0.00000
25% 4.000000 1.000000 48.95350
50% 11.000000 1.000000 201.17450
75% 33.000000 3.000000 518.81725
90% 72.000000 6.000000 1024.00000
max 1107.000000 143.000000 7573.38600