
    In an adaptive learning platform, develop an algorithm to identify the most impactful sequence of consecutive learning modules where the combined effect of student performance changes (improvements and setbacks) is maximized. The performance change for each module is represented by a score, where positive values indicate improvement and negative values indicate temporary setbacks, and the impact is measured by multiplying these consecutive scores together. This analysis will help determine the most significant period of learning progress or challenges in the student's journey.
    

    
def max_subarray_product(arr):
    """
    In an adaptive learning platform, develop an algorithm to identify the most impactful sequence of consecutive learning modules where the combined effect of student performance changes (improvements and setbacks) is maximized. The performance change for each module is represented by a score, where positive values indicate improvement and negative values indicate temporary setbacks, and the impact is measured by multiplying these consecutive scores together. This analysis will help determine the most significant period of learning progress or challenges in the student's journey.
    

    """
def max_subarray_product(arr):
    """
    In an adaptive learning platform, develop an algorithm to identify the most impactful sequence of consecutive learning modules where the combined effect of student performance changes (improvements and setbacks) is maximized. The performance change for each module is represented by a score, where positive values indicate improvement and negative values indicate temporary setbacks, and the impact is measured by multiplying these consecutive scores together. This analysis will help determine the most significant period of learning progress or challenges in the student's journey.
    

    """
    n = len(arr)
    max_ending_here = 1
    min_ending_here = 1
    max_so_far = 0
    flag = 0
    for i in range(0, n):
        if arr[i] > 0:
            max_ending_here = max_ending_here * arr[i]
            min_ending_here = min(min_ending_here * arr[i], 1)
            flag = 1
        elif arr[i] == 0:
            max_ending_here = 1
            min_ending_here = 1
        else:
            temp = max_ending_here
            max_ending_here = max(min_ending_here * arr[i], 1)
            min_ending_here = temp * arr[i]
        if max_so_far < max_ending_here:
            max_so_far = max_ending_here
    if flag == 0 and max_so_far == 0:
        return 0
    return max_so_far