
    In an adaptive learning platform, write a function that analyzes a sorted sequence of student performance scores to determine if a specific score represents the predominant achievement level. Given a sorted array of historical student scores, the total number of attempts, and a target score, determine if this target score appears in more than half of all student attempts. This analysis will be used to automatically adjust module difficulty based on the most common student performance level.
    

    
def binary_search(arr, low, high, x):
    if high >= low:
        mid = (low + high) // 2
        if (mid == 0 or x > arr[mid - 1]) and arr[mid] == x:
            return mid
        elif x > arr[mid]:
            return binary_search(arr, mid + 1, high, x)
        else:
            return binary_search(arr, low, mid - 1, x)
    return -1

def is_majority(arr, n, x):
    """
    In an adaptive learning platform, write a function that analyzes a sorted sequence of student performance scores to determine if a specific score represents the predominant achievement level. Given a sorted array of historical student scores, the total number of attempts, and a target score, determine if this target score appears in more than half of all student attempts. This analysis will be used to automatically adjust module difficulty based on the most common student performance level.
    

    """
def binary_search(arr, low, high, x):
    if high >= low:
        mid = (low + high) // 2
        if (mid == 0 or x > arr[mid - 1]) and arr[mid] == x:
            return mid
        elif x > arr[mid]:
            return binary_search(arr, mid + 1, high, x)
        else:
            return binary_search(arr, low, mid - 1, x)
    return -1

def is_majority(arr, n, x):
    """
    In an adaptive learning platform, write a function that analyzes a sorted sequence of student performance scores to determine if a specific score represents the predominant achievement level. Given a sorted array of historical student scores, the total number of attempts, and a target score, determine if this target score appears in more than half of all student attempts. This analysis will be used to automatically adjust module difficulty based on the most common student performance level.
    

    """
    i = binary_search(arr, 0, n - 1, x)
    if i == -1:
        return False
    if i + n // 2 <= n - 1 and arr[i + n // 2] == x:
        return True
    else:
        return False