
    In an adaptive learning system, determine the number of unique ways a student can progress through n learning modules while experiencing exactly m difficulty increases between consecutive modules, where a difficulty increase occurs when a later module has a higher complexity rating than its predecessor. This calculation helps optimize the sequencing of personalized learning content and assessment difficulty levels.
    

    
def eulerian_num(n, m):
    """
    In an adaptive learning system, determine the number of unique ways a student can progress through n learning modules while experiencing exactly m difficulty increases between consecutive modules, where a difficulty increase occurs when a later module has a higher complexity rating than its predecessor. This calculation helps optimize the sequencing of personalized learning content and assessment difficulty levels.
    

    """
def eulerian_num(n, m):
    """
    In an adaptive learning system, determine the number of unique ways a student can progress through n learning modules while experiencing exactly m difficulty increases between consecutive modules, where a difficulty increase occurs when a later module has a higher complexity rating than its predecessor. This calculation helps optimize the sequencing of personalized learning content and assessment difficulty levels.
    

    """
    if m >= n or n == 0:
        return 0
    if m == 0:
        return 1
    return (n - m) * eulerian_num(n - 1, m - 1) + (m + 1) * eulerian_num(n - 1, m)