Hello! I am a little confused about when to use the ANCOVA vs. when to decide for a multiple regression and would really appreciate your suggestions to help me understand this better!

In a study that I'm currently reading the researchers are comparing two groups of students (experimental and control group). Prior to the intervention the students's success performance is measured with a multi-item self-report scale. The methods part describes: "The data were analyzed using multiple regression with dummy codes representing the nesting of students, teachers, and schools. The focal predictor war the interaction between the dummy code for experimental condition (0 = control, 1= relevance) and student's performance expectation for the course. We predicted that this interaction term would be negative, such that the intervention effect would be more positive for those with low as opposed to high performance expectations".

So, I assume that the variable "performance expectation" acts somehow as a moderator here, which is continuous while the group variable is clearly categorical.

However, I don't understand why the researchers decided to use the multiple regression instead of ANCOVA and what the advantages of this method are in this specific case.

Further the authors conclude in the results part: "The predicted values from the regression equation indicate that students with low success expectancies (one standard deviation below the mean) reported more interest in science at the end of the semester in the relevance condition than in the control condition, whereas students with high success expectancies (one standard deviation above the mean) reported similar level of interest regardless of experimental condition. Do I get it right that they are creating two categories here (out of the continuous variable)by putting numbers in the regression equation in order to show the interaction very clearly in their graph?

Thank you so much for your advice!