
    Design an algorithm to identify the N most effective learning combinations by pairing each student's theoretical assessment score with an engagement metric from their practical exercises, where each combination's effectiveness is determined by multiplying the two scores together. The algorithm must select one score from the theoretical assessments list and one from the practical exercises list to form each combination. Each identified combination should represent a potential personalized learning path that maximizes the student's learning effectiveness.
    

    
def large_product(nums1, nums2, N):
    """
    Design an algorithm to identify the N most effective learning combinations by pairing each student's theoretical assessment score with an engagement metric from their practical exercises, where each combination's effectiveness is determined by multiplying the two scores together. The algorithm must select one score from the theoretical assessments list and one from the practical exercises list to form each combination. Each identified combination should represent a potential personalized learning path that maximizes the student's learning effectiveness.
    

    """
def large_product(nums1, nums2, N):
    """
    Design an algorithm to identify the N most effective learning combinations by pairing each student's theoretical assessment score with an engagement metric from their practical exercises, where each combination's effectiveness is determined by multiplying the two scores together. The algorithm must select one score from the theoretical assessments list and one from the practical exercises list to form each combination. Each identified combination should represent a potential personalized learning path that maximizes the student's learning effectiveness.
    

    """
    result = sorted([x * y for x in nums1 for y in nums2], reverse=True)[:N]
    return result