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cmaclellcmaclell
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added streak controller
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‎al_outerloop/controllers/streak.py‎

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from .base import OuterLoopController
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from random import choice
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import json
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import sys,os,inspect
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import colorama
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from colorama import Fore, Back, Style
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colorama.init(autoreset=True)
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#######
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# This version of the BKT controller updates
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# mastery probabilities by treating every interface component
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# (in the Selection field) as a separate KC, and choosing a
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# problem with the most unmastered KCs. It must be used with
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# an action_space that specifies the KCs in the problem set.
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# Chooses a problem with the maximum number of unm
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#
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# Options specifiable via outerloop_args:
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# bkt_probs (required) : Specifies the BKT probabilities for each skill:
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# - known: the probability the skill is already learned before any practice attempts
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# - learn: the probability of learning the skill on a single practice attempt
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# - guess: the probability a learner will answer correctly despite not knowing a skill
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# - slip: the probability a learner will answer incorrectly despite knowing a skill
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# num_test_problems : number of problems to use for testing (not training);
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# these problems will be the final problems specified in the action_space.
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# interface_to_skill : a mapping between each interface component and skill
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# If not included, raw interface components are skills. If this maps everything
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# to an empty string, than include_problem_start_in_kc can be used to map
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# all steps in a problem to the same skill.
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# single_kc : all attempts use the same kc
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# include_problem_start_in_kc : in addition to the interface component
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# skills will include any part of the problem name that occurs before a space,
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# or if num_chars_problem_start is included as well, that number of characters
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# from the problem name
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#######
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class Streak(OuterLoopController):
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def __init__(self):
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super().__init__()
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def new_student(self, student_id, action_space=None, outer_loop_args = None):
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super().new_student(student_id, action_space)
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self.kcs = outer_loop_args['kcs']
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self.interface_to_kc = outer_loop_args['interface_to_kc']
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self.correct_counts = {kc: 0 for kc in self.kcs}
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self.num_problems = 0
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self.choose_max_unmastered = True
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if 'choose_max_unmastered' in outer_loop_args:
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self.choose_max_unmastered = outer_loop_args['choose_max_unmastered']
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# default streak threshold
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self.streak_threshold = 3
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if 'streak_threshold' in outer_loop_args:
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self.streak_threshold = outer_loop_args['streak_threshold']
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# default max problems of 150
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self.max_problems = 150
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if 'max_problems' in outer_loop_args:
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self.max_problems = outer_loop_args['max_problems']
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# Whether we're in testing or training - we'll only start in test mode if
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# the probability known is higher than the threshold for all skills.
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# (This generally shouldn't happen.)
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self.test_mode = False # We start off in training mode
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# Map each problem to a skill
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self.problems_by_skill = {}
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for problem in self.action_space:
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kcs = self.get_problem_kcs(problem)
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for kc in kcs:
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if kc not in self.problems_by_skill:
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self.problems_by_skill[kc] = []
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self.problems_by_skill[kc].append(problem)
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# print("self.problems_by_skill")
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# print(self.problems_by_skill)
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def resolve_kcs(self, step):
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kc_list = self.current_prob['kc_list']
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kc_list2 = self.interface_to_kc[step]
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return set(kc_list).intersection(set(kc_list2))
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def update(self, step, reward, action_type):
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# 0/1 for correct incorrect rather than a string to print
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correctness_numeric = 1 if reward > 0 and action_type == "ATTEMPT" else 0
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if(action_type == "ATTEMPT"):
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correctness = Back.GREEN + "correct" if reward > 0 else Back.RED + "incorrect"
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else:
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correctness = Back.BLUE + "example"
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# Print out information about performance
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print(Fore.CYAN + "Streak Controller Udpate:", step, reward, correctness)
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# This controller updates the knowledge component based on the interface marked in the
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# Selection field - here, that corresponds to the step variable.
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kcs = self.resolve_kcs(step)
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for skill in kcs:
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if skill not in self.correct_counts:
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print("ERROR:", skill, "not included in kc_list adding... (",
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problem_name, ", ", step, ")")
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self.correct_counts[skill] = 0
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if correctness_numeric == 1:
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self.correct_counts[skill] += 1
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else:
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self.correct_counts[skill] = 0
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print("Correctness count for %s after update: %i" % (skill, self.correct_count[skill]))
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def get_problem_kcs(self, problem):
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if("kc_list" in problem):
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kcs = problem["kc_list"]
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else:
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raise ValueError("Problem objects must have attribute 'kc_list'")
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return kcs
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def all_skills_mastered(self):
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'''
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Returns true if all skills that we have problems for are above mastery threshold
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'''
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for skill in self.correct_counts:
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if (skill in self.problems_by_skill and
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self.correct_counts[skill] <= self.streak_threshold):
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return False
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return True
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def next_problem(self,student=None):
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self.num_problems += 1
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if self.all_skills_mastered() or len(self.rewards) > max_problems:
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# All skills have been mastered or we've asked as many
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# problems as allowed - stop training.
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self.test_mode = True;
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print("Mastery estimates when entering testing:", self.correct_counts)
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print("Skills mastered:", self.all_skills_mastered())
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if self.test_mode:
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if len(self.test_set) > 0:
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nxt = self.test_set.pop(0)
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nxt["test_mode"] = True
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return nxt
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else:
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print("Problems in training ( total number =", len(self.problems_asked),")")
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print(self.problems_asked)
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return None # done training
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else:
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print("Asking for problem ", self.num_problems)
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print("Mastery_Probabilities: ",self.correct_count)
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# Choose a problem with the most unmastered skills
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max_unmastered_kcs = 0
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problem_with_max_unmastered_kcs = []
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for problem in self.action_space:
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kcs = self.get_problem_kcs(problem)
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# if bkt_config.get("single_kc", False):
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# skills = problem["kc_list"]
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# else:
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# skills = ["single_kc"]
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# Check how many skills that are used in this problem are unmastered
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unmastered_kcs = [kc for kc in kcs if self.correct_counts[kc] < self.streak_threshold]
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if self.choose_max_unmastered:
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if len(unmastered_kcs) > max_unmastered_kcs:
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max_unmastered_kcs = len(unmastered_kcs)
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problem_with_max_unmastered_kcs = [problem]
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elif len(unmastered_kcs) == max_unmastered_kcs: # We'll choose randomly among problems with the same number of unmastered skills
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problem_with_max_unmastered_kcs.append(problem)
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else:
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problem_with_max_unmastered_kcs.append(problem)
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# print("Number of problems with", max_unmastered_kcs, "unmastered skills:", len(problem_with_max_unmastered_kcs))
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# Choose a random problem from problems with the maximum unmastered skills
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nxt = choice(problem_with_max_unmastered_kcs)
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self.problems_asked.append(nxt["question_file"])
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self.current_prob = nxt
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# print("N",nxt)
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return nxt
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‎al_outerloop/server.py‎

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print(sys.path)
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from controllers.random import Random
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from controllers.bkt import BKT
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from controllers.bkt import Streak
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def str_to_class(s):
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return getattr(sys.modules[__name__], s)
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‎examples/outer_loop_test_streak.json‎

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{
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"set_params" : {
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"agent" : {
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"agent_type": "ModularAgent",
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"stay_active": true,
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"dont_save": true,
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"args": {
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"planner" : "numba",
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"when_learner": "trestle",
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"where_learner": "FastMostSpecific",
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"state_variablization" : "whereappend",
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"strip_attrs" : ["to_left","to_right","above","below","type","id","offsetParent"],
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"when_args" : {
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"cross_rhs_inference" : "none"
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},
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"function_set" : ["RipFloatValue","Add", "Subtract","Numerator_Multiply", "Cross_Multiply", "Divide"],
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"feature_set" : ["Equals"]
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}
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},
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"HTML": "tutors/FractionArithmetic/fraction_arithmetic.html",
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"abs_qf_paths": true
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},
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"training_set1": [
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{
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"agent_name": "Streak Agent",
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"repetitions" : 100,
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"outer_loop_controller" : {
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"type" : "Streak",
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"args" : {
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"streak_threshold": 3,
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"max_problems": 150,
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"choose_max_unmastered": True,
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"kcs": ["AD check_convert","AD den3","AD num3","AD den4",
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"AD num4","AD den5","AD num5","AD done","AS den5",
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"AS num5","AS done","M den5","M num5","M done"],
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"interface_to_kcs" : {
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"check_convert" : ["AD check_convert"],
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"done" : ["AD done", "AS done", "M done"],
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"JCommTable4.R0C0" : ["AD num3", "AS num3", "M num3"],
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"JCommTable4.R1C0" : ["AD den3", "AS den3", "M den3"],
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"JCommTable5.R0C0" : ["AD num4", "AS num4", "M num4"],
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"JCommTable5.R1C0" : ["AD den4", "AS den4", "M den4"],
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"JCommTable6.R0C0" : ["AD num5", "AS num5", "M num5"],
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"JCommTable6.R1C0" : ["AD den5", "AS den5", "M den5"],
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}
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},
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"problem_set": {
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"concatenate" : [
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{"glob" :
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{"key" : "question_file",
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"pattern": "tutors/FractionArithmetic/mass_production_brds/AD*",
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"obj_template" : {"kc_list" : ["AD check_convert","AD den3","AD num3","AD den4","AD num4","AD den5","AD num5","AD done"]}
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}
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},
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{"glob" :
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{"key" : "question_file",
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"pattern": "tutors/FractionArithmetic/mass_production_brds/AS*",
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"obj_template" : {"kc_list" : ["AS den5","AS num5","AS done",]}
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}
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},
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{"glob" :
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{"key" : "question_file",
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"pattern": "tutors/FractionArithmetic/mass_production_brds/M*",
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"obj_template" : {"kc_list" : ["M den5","M num5","M done"]}
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}
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}
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]
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}
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},
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}
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]
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}

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